Volume 8, Number 3, July 2022 IEEE Systems, Man and Cybernetics Magazine

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CALL FOR PAPERS IEEE Systems, Man, and Cybernetics Magazine Special Issue on Advancements, Challenges and Application of Parallel and Distributed algorithms I.

AIM AND SCOPE

Advancements in technology have resulted in development of highly complex, integrated, and sophisticated systems and applications to cater to various industrial demands. The backbone for a highly successful and advanced system is the choice and usage of algorithms which are functioned. With the increased use of several distributed and cloud-based platforms and applications it is understandable that the significance of parallel algorithms for developing distributed applications has increased by leaps and bounds. Parallel algorithms possess the capacity to execute multiple instructions simultaneously across different processing units or devices and then consolidate and combine the output from each unit to produce one final output. Parallel algorithms play a pivotal role in determining and configuring the architecture of any distributed system. Also, the hardware and software requirements to develop and integrate a distributed system are highly reliant on the parallel algorithms. The major challenges in developing and using parallel algorithms for distributed computing system lie in integrating heterogeneous sub-systems, resources and executing the algorithms to derive an output along with an eye on various performance attributes and metrics. This special issue is initiated with a focus towards integrating the recent research work and studies related to developing parallel algorithms for distributed computing systems. This special issue provides a perfect forum to discuss the new and trending methodologies, approaches, advancements, and enhancements related to developing parallel algorithms.

II.

TOPICS

The topics relevant to the special issue include (but are not limited to) the following topics: • • • • • • • • •

Performance analysis of various parallel algorithms Advanced methodologies and practices for developing parallel algorithms in distributed computing applications High performance computing using parallel algorithms Resource intensive computing using parallel algorithms Integration and dependency issues in parallel algorithms for distributed computing Parallel algorithms for Big Data Analytics Parallel algorithms in distributed IoT Parallel algorithms with middleware for parallel and distributed applications Parallel complexity theory and algorithms

Digital Object Identifier 10.1109/MSMC.2022.3177330

• • • • • • • •

Time and schedule intensive parallel algorithms for computing Interaction of parallel algorithms with heterogeneous sub-systems Architectural complexity of parallel algorithms Concurrency and non-functional requirements in parallel computing Parallel algorithms for OpenMP and In-memory databases MPI for parallel programming for distributed and parallel applications Programming models and compilers and runtimes for distributed and parallel applications Parallel programming for Emerging Many-Core Parallel Programming Models

III.

SUBMISSIONS

Manuscripts should be formatted according to the IEEE SMC Magazine guidelines for authors (https://www.ieeesmc.org/publications/smc-magazine), and submitted through the IEEE’s Manuscript Central (https://mc.manuscriptcentral.com/smcmag). Please select “Special Issue” under Manuscript Category of your submission.

IV.

IMPORTANT DATES

Manuscript submissions due: Oct. 30, 2022 First round of reviews completed: Dec. 15, 2022 Revised manuscripts due: Jan 15, 2023 Second round of reviews completed: Feb 15, 2023 Final manuscripts due: March 31, 2023

V.

GUEST EDITORS

Dr. Suresh Chandra Satapathy Professor Kalinga Institute of Industrial Technology, India Email: [email protected] Sos Agaian The City University of New York, New York, USA. E-mail: [email protected] Dr. Jinshan Tang Professor George Mason University, Fairfax, VA, USA Email: [email protected] Dr. Milan Simic

Senior Lecturer School of Engineering RMIT University, Melbourne, Australia Email: [email protected] Dr. Yu-Dong Zhang Professor University of Leicester, Leicester, LE1 7RH, UK Email: [email protected] Dr. Vedula VSSS Chakravarthy Professor, Raghu Institute of Technology, Visakhapatnam, AP, India E-mail: [email protected]

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Ju ly 2022

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Smart Solutions for Technology

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Volume 8, Number 3 • July 2022

Features 8 Evolution in Computing Paradigms for Internet of Things-Enabled Smart Grid Applications

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Their Contributions to Power Systems By Fargol Nematkhah, Farrokh Aminifar, Mohammad Shahidehpour, and Sasan Mokhtari

21 Toward Named Data Networking An Approach Based the Internet of Things Cloud With Edge Assistance By Xiaonan Wang and Xinyan Qian

28 The Need for Quantification of Uncertainty

in ­Artificial Intelligence for Clinical Data Analysis Increasing the Level of Trust in the Decision-Making Process By Moloud Abdar, Abbas Khosravi, Sheikh Mohammed Shariful Islam, U. Rajendra Acharya, and Athanasios V. Vasilakos

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ABOUT THE COVER The evolution in computing paradigms contributes to power systems.

41 Impulsive Consensus of Fractional-Order Takagi–Sugeno Fuzzy Multiagent Systems With Average Dwell Time Approach and Its Applications Achieving Finite-Time Consensus By G. Narayanan, M. Syed Ali, Jianan Wang, Syeda Asma Kauser, Ahmed A. Zaki Diab, and H.I. Abdul Ghaffar

51 Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression Simplifying Complex T2F Calculations By Assef Zare, Afshin Shoeibi, Narges Shafaei Bajestani, Parisa Moridian, ­Roohallah Alizadehsani, Majid Hallaji, and Abbas Khosravi

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61 Applying an MSVR Method to Forecast a Three-Degree-of-Freedom Soft Actuator for a Nonlinear Position Control System Simulation and Experiments By Toru Usami and Mingcong Deng

Departments & Columns Mission Statement The mission of the IEEE Systems, Man, and Cybernetics Society is to serve the interests of its members and the community at large by promoting the theory, practice, and interdisciplinary aspects of systems science and engineering, human–machine systems, and cybernetics. It is accomplished through conferences, publications, and other activities that contribute to the professional needs of its members.



6 Editorial 70 Meet Our Volunteers 73 Conference Reports Digital Object Identifier 10.1109/MSMC.2022.3181873

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IEEE Systems, Man, and Cybernetics Magazine EDITOR-IN-CHIEF Haibin Zhu Nipissing University, North Bay, Ontario, Canada [email protected]

ASSOCIATE EDITORS

Okyay Kaynak, Vice President, Organization and Planning Shun-Feng Su, Vice President, Publications Ying (Gina) Tang, Vice President, Finance Vladik Kreinovich, Treasurer

Mali Abdollahian, Australia Mohammad Abdullah-Al-Wadud, Saudi Arabia Choon Ki Ahn, Korea Bernadetta Kwintiana Ane, India Krishna Busawon, UK György EIgner, Hungary Liping Fang, Canada Giancarlo Fortino, Italy Hossam Gaber, Canada Aurona Gerber, South Africa Jason Gu, Canada Abdollah Homaifar, USA Okyay Kaynak, Turkey Kevin Kelly, Ireland Kazuo Kiguchi, Japan Abbas Khosravi, Australia Vladik Kreinovich, USA Wei Lei, China Kovács Levente, Hungary Xiaoou Li, Mexico Darius Nahavandi, Australia Chris Nemeth, USA Vinod Prasad, Singapore Hong Qiao, China Ferat Sahin, USA Mehrdad Saif, Canada Bahram Shafai, USA Weiming Shen, Canada Liqiong Tang, New Zealand Ying Tan, Australia Yingxu Wang, Canada Margot Weijnen, Netherlands Peter Whitehead, USA Zhao Xingming, China Laurence T. Yang, Canada Qiangfu Zhao, Japan

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SOCIETY BOARD OF GOVERNORS

Publications Ethics Committee Shun-Feng Su, Chair Imre Rudas Edward Tunstel Vladik Kreinovich Peng Shi Fei-Yue Wang Robert Kozma Ljiljana Trajkovic Haibin Zhu

Executive Committee Sam Kwong, President Imre Rudas, Jr. Past President Edward Tunstel, Sr. Past President Enrique Herrera Viedma, Vice President, Cybernetics Saeid Nahavandi, Vice President, Human–Machine Systems Thomas I. Strasser, Vice President, Systems Science and Engineering Yo-Ping Huang, Vice President, Conferences and Meetings Karen Panetta, Vice President, Membership and Student Activities

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Ju ly 2022

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Editorial

by Haibin Zhu

More Diverse Investigations and More Collaborations Make More Contributions to the Community

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hanks to all the contributing authors for this issue. We collected six high-quality technical articles for it, which signified a new pace of the development of IEEE Systems, Man, and Cybernetics Magazine. I hope that we continue this pace to advance the magazine and provide more innovative technological articles to our readers, with the support of energetic author groups. This issue presents articles across multiple disciplines. I believe the content can reach and attract varieties of readers across the IEEE Systems, Man, and Cybernetics Society. The presented research results are from the fields of computing, the Internet of Things (IoT), artificial intelligence (AI), data analytics, and control technology. The other important feature of this issue is that half of the articles were produced by international collaborations, demonstrating the effectiveness and significance of collaborative research. The first article in this issue, “Evolution in Computing Paradigms for Internet of Things-Enabled Smart Grid Applications: Their Contributions to Power Systems,” is a collaboration among different countries and fields, where reDigital Object Identifier 10.1109/MSMC.2022.3178557 Date of current version: 15 July 2022

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Haibin Zhu

searchers M. Shahidehpour and Farrokh Aminifar are from the University of Tehran, Iran; Fargol Nematkhah is from the Illinois Institute of Technology, Chicago; and Sasan Mokhtari is from industry through Open Access Technology International, Minneapolis, Minnesota. The authors argue that an excessive number of connected devices and unprecedented large volumes of data have triggered significant advances in computing technologies to leverage collected data for establishing novel services. Therefore, cloud, edge, and fog solutions have been developed to provide data processing, storage, and networking to

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2022

fulfill ever-changing application requirements. They present attempts to push data processing closer to the edge of networks, where potential data producers and consumers reside. They think that the evolving paradigm of computing methods has enabled innovative data-based solutions in a variety of sectors, such as services, economics, energy, and production. They predict that power systems are undergoing transformative changes with computational shifts. They deduce that extensive perception of nondispatchable generation resources, electric vehicles, and storage systems takes place at various levels of power systems, especially at the edge of the grid. To catch up with technological evolution, the authors believe that distributed frameworks enable the management and alignment of consumer-owned energy resources to maintain electricity’s secure and reliable provision. They argue that emerging computing paradigms have substantially contributed to a seamless interface among normal power systems and that the future grid will be realized under the smart grid paradigm. They illustrate transformations of computing patterns in chronological order, including associated architectures, such as clouds, edges, and fogs; use cases in various infrastructures; and support to power systems. They also discuss quantum computing as a direction of future research, which can massively impact the energy sector. The second article, “Toward Named Data Networking: An Approach Based the Internet of Things Cloud With Edge Assistance,” is contributed by Xiaonan Wang and Xinyan Qian, Changshu Institute of Technology, China. The authors believe that the IoT aims to improve the quality of human life by delivering collected data efficiently for real-time monitoring. With the increasing complexity of data, it is hard for an individual IoT device to produce information by

ods while applying various AI proceitself due to restrictions of visual angles dures. At first, they briefly discuss a few and resources. For instance, the camera important applications of AI methods in mounted on the front of a vehicle capdifferent areas. Thereafter, they present tures only the data from the road ahead; the definition of uncertainty in AI methit cannot collect information from the ods and techniques to deal with it. They side. The local cloud (LC) is a new then describe the main UQ and calibracommunication paradigm, where cloud tion for AI methods in clinical decision members collaboratively generate data making. They then delocally by sharing scribe key directions resources. Thus, the and functions of UQ integration of the IoT The other impormethods, major chaland LC (ITLC) is an tant feature of lenges, and how these effective way to overthis issue is that are related to the precome the resource vious work on UQs. restrictions of an indihalf of the articles Then, they discuss the vidual device. Named were produced by foremost research ardata networking international coleas to apply UQ meth(NDN) is a novel and laborations. ods in medical data efficient communicaanalysis. Finally, they tion mechanism, and offer conclusions by its features can assist providing clinical implications and clariin realizing the ITLC and enhancing the fying the requirement for UQ in AI methefficiency of ITLC-based data delivery. ods to facilitate medical data analysis. However, the authors point out that Next comes “Impulsive Consensus NDN has different architectures and of Fractional-Order Takagi–Sugeno features from the ITLC. Therefore, it is Fuzzy Multiagent Systems With Avchallenging to exploit NDN to make the erage Dwell Time Approach and Its ITLC a reality. They propose an edgeApplications: Achieving Finite-Time assisted NDN-based ITLC framework Consensus,” by M. Syed Ali, G. Narayand use evaluation results to verify the anan, Jianan Wang, Syeda Asma Kauser, advances of the framework. H.I. Abdul Ghaffar, and Ahmed A. Zaki “The Need for Quantification of UnDiab, a collaborative team of researchcertainty in Artificial Intelligence for ers from India, China, Saudi Arabia, and Clinical Data Analysis: Increasing the Egypt. They study the Takagi–Sugeno Level of Trust in the Decision-Making (T–S) fuzzy-based impulsive consensus Process” is authored by Moloud Abdar, problem of a fractional-order multiagent Abbas Khosravi, Sheikh Mohammed system (FOMAS) with an average dwell Shariful Islam, U. Rajendra Acharya, time (ADT). FOMASs are nonlinear sysand Athanasios V. Vasilakos, another tems and can be modeled as linear subinternational research team represystems by using a T–S fuzzy model. The senting Deakin University, Australia; authors investigate a T–S fuzzy FOMAS Singapore University of Social Sciences; that is subject to a class of impulse time and Fuzhou University, China. In this arsequences with the ADT approach. They ticle, trust and certainty are considered propose an impulsive control scheme to important elements in the research of AI make the tracking error converge in a applications. The authors assume that finite-time consensus into a small neighthe level of trust in AI can be measured borhood of origin. Based on impulsive and achieved by distinguishing uncertainties in predicting AI methods used fractional differential equations theory, in medical studies. They believe that it they design a Lyapunov functional apis necessary to propose effective uncerproach with the ADT technique, resulttainty quantification (UQ) and measureing in an impulsive controller to achieve ment methods to have trustworthy AI finite-time consensus of a T–S fuzzy FOclinical decision support systems. MAS. They use practical examples to The authors present practical guideensure the effectiveness and superiority lines for developing and using UQ methof the proposed approach.

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Article five is “Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression,” by Assef Zare, Afshin Shoeibi, Narges Shafaei Bajestani, Parisa Moridian, Roohallah Alizadehsani, Majid Hallaji, and Abbas Khosravi, a research team from Australia. The authors present a new type 2 fuzzy (T2F) regression model to avoid high complexity by considering the more general form of T2F membership functions. The performance of the developed model is evaluated using the Taiwan Stock Exchange Capitalization Weighted Stock Index and COVID-19 forecasting data sets. The developed model reaches the highest performance when compared with the other state-of-the-art techniques. The developed method is ready to be tested with more uncertain data and has the potential to predict weather and stock values. Finally, “Applying a MSVR Method to Forecast a 3D Soft Actuator for a Nonlinear Control System: Simulation and Experiments” is by Toru Usami and Mingcong Deng, Tokyo University of Agriculture and Technology. The authors propose a new method to provide tip position coordinate control of a three-degree-of-freedom soft actuator. Such a soft actuator consists of three artificial muscles and can facilitate more complex motions than conventional soft actuators. The proposed model and control system can handle multiple-input patterns, and therefore various motions are possible. In addition, the authors use a machine learning technique called multioutput support vector regression to compensate for the complexity of multiple-input, multiple-output systems. They verify the proposed system through experiments. The magazine welcomes more diverse and collaborative research from all the involved disciplines throughout the Society. About the Author Haibin Zhu ([email protected]) is a full professor and the coordinator of the Computer Science Program, the founding director of the Collaborative Systems Laboratory, and a member of the Arts and Science Executive Com­ mittee, Nipissing University, North Bay, Ontario, P1B 8L7, Canada.

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Evolution in Computing Paradigms for Internet of Things-Enabled Smart Grid Applications Their Contributions to Power Systems

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he excessive number of connected devices and the unprecedented large volumes of data have triggered significant advancements in computing technologies to leverage the collected data for establishing novel services. Accordingly, cloud, edge, and fog notions have been developed to provide data processing, storage, and networking to fulfill the ever-changing application requirements. Recent efforts have attempted to push data processing closer to the edge of the network, where potential data producers and consumers reside. The evolving paradigm of computing methods has enabled innovative data-based solutions in health-care, industry, transportation, and energy domains for societal, economic, and productivity enhancements. Along with the computational shifts, Digital Object Identifier 10.1109/MSMC.2022.3162696 Date of current version: 15 July 2022

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by Fargol Nematkhah, Farrokh Aminifar, Mohammad Shahidehpour, and Sasan Mokhtari power systems are undergoing transformative changes. Extensive penetration of nondispatchable generation resources, electric vehicles (EVs), and storage systems is occurring at various levels of power systems, particularly at the edge of the grid. In response to such changes, distributed frameworks have been widely proposed to enable the management and alignment of consumer-owned energy resources to maintain the secure and reliable provision of electricity. The emerging computing paradigms have substantially contributed to a seamless interface between the conventional power systems and the future grid that is realized under the smart grid paradigm. In this article, we discuss the chronological transformations in computing schemes and their contributions to power systems. Accordingly, the concepts of cloud, edge, and fog/edge are put forth. Moreover, quantum computing is discussed as a line of future research that can massively impact the energy sector, albeit in a different manner.

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The Internet of Things With the advancements in technology, processing and storage resources can be embedded in everyday objects,



enabling them to be intelligent components of a dynamic network. Considering the vast number of heterogeneous things that are connected to the Internet today, it seems that Nikola Tesla’s idea of the connected world in 1926 is turning into reality. However, the term Internet of Things (IoT) was first adopted by Kevin Ashton in 1999 when introducing radio-frequency identification into the context of supply chain management. He proposed that data gathered automatically from things can be interpreted into insightful information to improve observability and efficiency. Despite the fact that the concept of the IoT was first envisioned for supply chain management, its extensions are now contributing to a wide range of fields, including but not limited to health care and well-being, transportation, energy, and education. Accordingly, new concepts are emerging to better denote a network of intelligent objects based on the scope of their application. The Industrial Internet of Things and the Internet of Energy are such notions, to name a few. The Internet of Everything is another concept defined by Cisco, extending the IoT as a network of physical objects to people, processes, and data. The extensive connectivity in the Internet of Everything is assumed to create value-added services to individuals, companies, and communities. A broad range of IoT definitions exists in the literature, which explains the ongoing evolution of this phenomenon. The IEEE IoT Initiative has proposed various definitions for this concept based on the scale of the systems adopting the IoT and continues to contribute to the ever-changing terminology. We consider a consolidated definition of IoT throughout this article as “a world of interconnected things, which are capable of sensing, actuating, and communicating among themselves i.e., smart things or smart objects, and with the environment while providing the ability to share information and act in parts autonomously to real/physical world events and by triggering processes and creating services with or without direct human intervention” [1]. The enormous volumes of data gathered in IoT-based systems can be considered as the key factor reshaping applications and adding value to services in an unprecedented way. However, it is not the data themselves that create value but the analytics performed on the data. Figure 1 illustrates the transformation of data received from things to decision-making signals. Cloud Computing Transforming raw data into insightful information requires nonvolatile memory, processing units, and realtime distribution of data to consumers. More sensitive applications might necessitate additional requirements, such as low latency, scalability, and confidentiality. Ju ly 2022

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IoT-based systems as the associated computational demand Despite various sets of resources embedded in everyday might be highly dynamic. Additionally, the availability proobjects, they often fail to meet the aforementioned requirevided through the network and standard mechanisms ments in a reliable and timely manner. Considering that allows clients to access the unlimitmost of these objects run on bated pool of resources using hetteries, a major bottleneck is energy erogeneous platforms, such as a constraints. Moreover, mains-powsmartphone, laptop, or workstaered devices lack the capabilities Extensive penetration tion. Furthermore, the clients needed for managing and analyzof nondispatchable have the opportunity to use the ing enormous quantities of data. To generation resources, latest services available, without fully exploit the envisioned potenconcerns about hardware or softtials of the IoT, particularly in electric vehicles, ware upgrades. large-scale systems, cloud technoland storage systems Services offered by cloud comogy has been integrated into the puting are categorized into three IoT context. is occurring at main threads: infrastructure as a The National Institute of Stanvarious levels of service (IaaS), platform as a serdards and Technology describes vice (PaaS), and software as a sercloud computing as “a model power systems, vice (SaaS). These services differ enabling ubiquitous, convenient, particularly at the in terms of the client’s control over on-demand network access to a edge of the grid. the cloud infrastructure, including shared pool of configurable comnetwork, server, operating system, puting resources (e.g., networks, storage, and application. While servers, storage, applications, and IaaS and PaaS are mostly deployed services) that can be rapidly proviby developers to execute their applications, SaaS provides sioned and released with minimal management effort or services to end users. service provider interactions” [2]. Considering the diverse use cases of cloud computing, The exceptional characteristics of cloud computing offer which might impose specific operation, management, and numerous advantages to users of IoT-based systems. From ownership requirements, the three deployment models of an economic perspective, in addition to the services offered private, hybrid, and public cloud are proposed. A private by the cloud, clients have unlimited access to the hardware cloud exclusively serves a single entity, while a public and software infrastructures used in a data center, without cloud is available to the general public. Accordingly, a having concerns about high investment or maintenance hybrid cloud is a combination of two or more clouds (pricosts. Moreover, the pay-as-you-go billing model considers vate or public) acting as a single unit and allowing data usage of the resources that were actually deployed by each and application portability. client. From a technological perspective, clients have unilateral access to a pool of resources, capable of adjusting to clients’ demands. This characteristic is crucial for specific Cloud Applications in a Smart Grid A smart grid is the paradigm of a power network, integrated with information and communication technologies, that is capable of delivering electricity to consumers in a more reliable, efficient, and cost-effective manner compared to traditional power systems. A smart grid features bidirecValue-Added tional flow of power and data between utilities and conService sumers to better accommodate the penetration of Knowledge renewable energy resources (RERs) into electric grids, which is rapidly increasing because of carbon emission Analytics restrictions. This cyberphysical system calls for a distribInformation uted architecture where intelligent agents can communicate w it h each ot her, ma ke decisions, a nd act autonomously. Realization of the smart grid paradigm Data introduces new technical challenges to the operation of Valueless Data power systems, which we believe are two-fold. ◆◆ Heterogeneity of devices: Smart grid characteristics necessitate the implementation of smart devices of various types and applications at all layers of the electric grid, such as home appliances, buildings, microgrids, substations, and the communication Figure 1. Data transformation. 10

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capacity offered by available and future RERs. Cloud network. Thus, a platform is required to facilitate the resources can advance power system planning by accelerintegration of a multitude of heterogeneous devices ating the extensive computations. through a unified protocol architecture and using the existing electric grid infrastructure. ◆◆ Heterogeneity of applications: Besides providing Demand-Side Management invaluable features, the smart grid concept has created The smart grid concept has significantly served demandnovel problems or introduced further intricacies to side management (DSM) applications, creating trementhe existing set of problems. For instance, realdous business opportunities for enterprises such as the t i me d y n a m ic pr ic i n g s c heme s , co or d i n a t e d NASDAQ-traded companies Opower and C3Energy, procha rg ing of EVs, and more viding value-added services to utilsophisticated extensions of ities and consumers. However, demand response, such as despite the computational needs of transactive energy, contribute network analysis applications, Fog computing offers to the heterogeneity of smart which tend to have small fluctuacloud capabilities grid applications. tions under a steady network conThe intrinsic characteristics of figuration, DSM applications call closer to end users clouds, particularly the scalability for scalable computation resources by making a cloudand the pay-as-you-go payment since the associated computational to-thing continuum, method, can effectively address needs rely highly on the number of many obstacles heightened by the resources to be scheduled, i.e., while edge heterogeneity of devices and appliflexible loads. Furthermore, in computing offloads cations in smart grids and, hence, most DSM applications, computahave introduced several cloudtion facilities need to grow in a the computational based solution concepts, which we polynomial manner with respect to burden of the cloud to discuss in the following sections. the number of resources. Consideredge devices. ing the fluctuating computational needs associated with DSM appliPower Dispatching cations, cloud computing can subCloud facilities can serve powerstitute for ex pensive centra l dispatching automation systems to information processing facilities, enabling small-size busiimprove the limitations caused by hardware resources in nesses, start-ups, and even individuals to contribute to this power systems where the information infrastructure canfield without incurring the high provisioning and maintenot keep up with the expansion pace of these systems. nance costs of computing resources. Accordingly, cloud platforms can transform the way largeMoreover, the data-sharing feature of cloud-based scale power system simulations are managed. Security, infrastructures can create a transparent platform where reliability, and fast recovery in response to extreme natudata can be deidentified and then made available to the ral disasters are crucial considerations for power-dispublic. This characteristic helps businesses and third parpatching systems and, if compromised, can lead to ties to identify the specific needs of consumers and develwide-area blackouts. The vulnerability of on-premise sysop customized intelligent applications accordingly. tems to natural disasters and cyberattacks has triggered Utilities can deploy cloud platforms to forecast the energy the idea of using virtual clouds and networks for powerdemand based on real data coming from the consumer dispatching automation purposes. end. Data-driven knowledge on future demand assists utilities in scheduling the optimal operation of energy Power System Planning resources and avoiding rolling blackouts. Cloud computing Cloud computing can assist independent system operators offers software platforms needed to incorporate the realwith planning problems. The widespread use of distributed time and historical records of electricity usage data in proenergy resources (DERs) in electric grids has added further gramming models that entail extensive computation. uncertainty to the expansion planning of these systems. Nevertheless, some smart grid applications necessitate Specifically, transmission network planning imposes more more stringent requirements that cloud-based platforms of a computational burden since various criteria, e.g., therfail to satisfy. We discuss the challenges arising in cloud mal, voltage, short-circuit, and electromagnetic transient systems in the following section. analyses, should be examined. Thus, system operators need to handle these challenges besides the nonlinearity of power system components, uncertainties on load forecasts, Cloud Computing Challenges forced outages of generation and transmission facilities, Despite numerous recognized advantages, cloud computing and emergency operating procedures. Moreover, the retirestill may not be a viable solution for many use cases. Transment of generation units will be tied to the generation ferring data over long distances between users and the cloud

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growth of IoT applications, most of which necessitate realtime or near-real-time response times as well as improved privacy and reliability. Also, the flow of data and task requests might change in IoT systems with a large number of edge devices. Figure 2 demonstrates the data and task flows in such paradigms. Accordingly, there exists a twoway task flow between devices and the cloud, meaning that devices not only request computing tasks and content from the cloud, but can provide services requested from the cloud. Storage, caching, and processing are some of the tasks that edge can perform. Fulfilling the growing challenges of IoT systems requires a distributed architecture of computation facilities located at the proximity of the network edge. In recent years, various technologies, such as geodistributed cloud computing, the mobile ad hoc network-based cloud, cloudlets, edge computing, and fog computing, have attempted to provide a decentralized platform comprising Edge Computing processing units at the edge of the frontier network to The widespread adoption of IoT devices has created a myrdecrease congestion and latency, besides enforcing privaiad of geodistributed data sources, such as smartphones, cy in emerging IoT applications. Among these technololaptops, sensors, actuators, and vehicles, which are congies, edge and fog computing have been given more stantly generating vast amounts of data. Additionally, the attention in academia and industry. Fog computing offers availability of IoT devices has amplified the fast-paced cloud capabilities closer to end users by making a cloudto-thing continuum, while edge computing offloads the computational burden of the cloud to edge devices. Accordingly, edge may seem to lean toward the device Cloud side, while fog tends to connote the infrastructure. The concept of edge computing was initially introduced in 2004 as a system that pushes program methods and the Database Database resulting data to the edge of the network to improve effiEdge ciency. However, the industrial appeal of this concept was actually triggered when fog computing was proposed. Edge computing is defined as “enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and Data Flow upstream data on behalf of IoT services” [3]. In this definiIoT Devices tion, an edge refers to any computing and network facility Task Flow between the initial data source and the destination data storage, e.g., a fog node or cloud data center. Figure 3 illustrates Figure 2. Data and task flows in the edge-integrated an edge-based parking-management system where a user computation paradigm. device, a smartphone, is added to the architecture as the edge layer to facilitate local decision making. The same architecture can be applied to charging stations to optimize the number of EVs flowing in and out of the station. Moreover, users can spot Local available parking spaces or charging staArea Bluetooth Wi-Fi Network tions faster when a user device, e.g., a smartphone, is engaged in the architecture. Smartphone While data processing methods and Switch Cloudlet technologies have been experiencing Server Car development surges one after the other, Occupancy network bandwidth and reliability have Sensors not kept up with this progressing pace. Therefore, in use cases where gigabytes of Figure 3. An edge-based parking management system. server introduces unpredictable latency, which prolongs analytics, would burden Internet service providers, and cause excess traffic in the network as well. Hence, in application scenarios, such as a smart home, where users are in the vicinity of where raw data are produced, excessive latency is introduced. Additionally, in applications where processing is done on an aggregate value of the collected data as well as use cases requiring uninterruptible connectivity to the cloud, such as smart vehicles, fire detection, and firefighting, central processing would be a better option compared to cloud computing. Finally, the stringent privacy and security regulations associated with some applications might enforce data to remain within specified juridical borders that cannot be fully met or examined precisely in cloud-based platforms. The same concerns exist in use cases involving sensitive data, such as smart health applications.

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data are generated within seconds, e.g., inflight Wi-Fi and autonomous vehicles, networking remains a bottleneck. In such cases, edge computing provides efficient processing with faster responses and a smaller network communication burden. For instance, running face recognition on a proof-ofconcept platform using edge computing instead of cloud computing can reduce the response time from 900 to 169 ms. Edge Applications in a Smart Grid Today, many infrastructures and systems are moving toward decentralized control for enhanced security, reliability, and cost-effectiveness. The banking world is a familiar example of such a transition. Here, blockchain technology is taking over to bypass third parties and enable secure trading among anonymous groups and individuals without them having to worry about their identity or physical location. Likewise, smart grids are transforming into a network of intelligent devices and DERs, including energy storage systems and RERs, controlled by their owners and spread throughout the distribution system. In this section, we initially discuss a number of smart grid paradigms, including active distribution networks (ADNs) and transactive energy, introduced to address the emerging operation management challenges of decentralization. We then inspect the role of edge computing in realizing such paradigms.

environmental and economic issues in addition to the conventional operation methods [4]. To effectively embrace the potentials of small-scale and decentralized generation capacities in ADNs, these systems can be treated as a network of smaller building blocks, such as microgrids, with the capability of autonomous decision making. A microgrid is defined as a group of DERs and loads that can act as a single controllable entity with two modes of configuration. A microgrid can be connected to a larger network through a point of common coupling, or it can operate in the islanded mode, in case of large disturbances, e.g., natural disasters or cyberattacks. The improved resilience, efficiency, scalability, and flexibility offered by microgrids as well as the available advanced control and information technologies in these systems can fulfill the multidirectional flows of power and data along with the high interoperability considered for an ADN. Therefore, a network of coupled microgrids can be regarded as a realization of the ADN paradigm. Coordinated operation of coupled microgrids can reduce the installed generation capacity by sharing the geographically dispersed DERs in each microgrid. Moreover, a microgrid can further enhance the resilience of an ADN. It can act as a backup power supply for the rest of the entity during major disruptions by initially islanding itself and, further, by scheduling its resources such as flexible loads and controllable generation to feed the critical loads of the ADN. Moreover, because of the self-sufficiency of microgrids, it is less likely for an ADN to experience a blackout since not all of the microgrids may fail to withstand severe contingencies simultaneously. Figure 4 depicts the decomposition of a 30-bus distribution system to four networked microgrids. An ADN as a network of connected microgrids requires a management scheme that would coordinate the operation of its elements. Central energy management schemes

ADNs and Networked Microgrids Extensive penetration of DERs into electric grids is changing the traditional means of electricity provisioning, which features unidirectional power flow from the upstream generation plants to downstream demand sites. Electricity consumers are being equipped with generation facilities, enabling them to not only supply their own demand, but to generate a surplus amount at times. This active role of consumers in modern distribution systems has created interoperability within the distribution system and encouraged the concept of prosumer as an electricity consumer MG 3 with generation capabilities. MG 1 DG The ADN paradigm is proposed as a 27 MG 2 cyberphysical system of electricity distri14 26 20 13 25 bution equipment, e.g., transformers, 19 12 24 18 feeders, lines, protective equipment, and MG 4 11 23 22 10 capacitors integrated with DERs, as well 1 2 3 4 5 6 7 8 9 Utility as control and communication systems DG Grid to effectively monitor and optimize the 15 multidirectional flows of power and data. 28 21 16 As the definition suggests, an ADN can 29 DG 17 30 be regarded as the electricity distribution system within a smart grid, contributing DG to the common objectives of resilience, MG: Microgrid sustainability, and efficiency. While benefiting electricity consumers with on-site generation, implementation of ADNs Figure 4. The decomposition of a 30-bus distribution network to four connected microgrids. DG: distributed generation. would challenge policies regarding

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power and data are accommodated under transactive involving a single coordinating authority, such as the distrienergy frameworks to allow the flexible resources to combution system operator (DSO), have been proposed in the pensate for the noncontrollable ones. The local manageliterature. However, in centralized frameworks, the two-way communication between the microgrids and the DSO would ment of power mismatches prevents burdening of the pose burdens, such as a large comnetwork and reverse f lows of munication overhead, that would power in case of a supply shortage disrupt real-time applications. and excess, respectively. FurtherMoreover, central energy manmore, transactive energy provides Transactive energy agement requires a comprehensive the opportunity to address the provides a marketview of the network, including the aforementioned challenges in a configuration and operation state of potentially less expensive way based platform all microgrid resources, which might compared to trading power with for voluntary not be available for the DSO because the main grid [6]. engagement of selfof privacy and security concerns. We discuss a potential scenario Distributed energy management of energy trading under a transaccontrolled resources schemes have been proposed as an tive energy framework, as illustratat the retail level to alternative solution for aligning the ed in Figure 5. Each resource decision-making process among under a transactive energy framemanage the realmicrogrids within an ADN without work should be associated with a time supply–demand requiring full observability and connode or an agent capable of maktrollability of the network. ing automated decisions a nd imbalance. communicating with other nodes. Consider the case where a proTransactive Energy sumer owning a solar photovoltaEnvisioned for power systems ic panel is facing an electricity generation deficiency with a high penetration of DERs, transactive energy because of forecast errors. To address this mismatch, aims to adjust the real-time operation of generation and the prosumer can conventionally supply power from the load resources with network conditions. The GridWise main grid or purchase power from its neighboring transArchitecture Council defines transactive energy as “a set of economic and control mechanisms that allow the active nodes. dynamic balance of supply and demand across the entire To take advantage of the novel energy provisioning electrical infrastructure using value as the key operaoffered in transactive energy frameworks, the prosumer tional parameter” [5]. announces the amount of required power and the respecIndeed, transactive energy provides a market-based tive price for it. Subsequently, transactive nodes, i.e., an platform for voluntary engagement of self-controlled EV, an energy storage system (ESS), and a residential proresources at the retail level to manage the real-time supsumer owning a wind turbine, who are willing to change ply–demand imbalance. Hence, bidirectional flows of their operation schedules, respond to the prosumer with their power and price offers. Prosumers will then make a transaction with the node offering the lowest price. Based on this scenario, only the trading nodes Transactive need to change their schedule to offset Node 2/ the mismatch while benefitting monetariESS ly from the transaction. If all neighboring nodes respond with a price offer greater Main Grid than the real-time electricity price, the Transactive prosumer will rationally buy power from Node 3/ the main grid. Prosumer Figure 6 represents a transactive energy platform where different resourcTransactive es exchange price and power data to Node 1/ Transactive reach a consensus on energy transacProsumer Node 4/ tions. As Figure 6 suggests, even though EV the data flow among scattered resources can be realized through information and communication technologies including the IoT, power transmission between Figure 5. A transactive energy market. ESS: energy storage system. 14

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stored and processed at each MGC for performing global any two arbitrary nodes might not be feasible because of energy management optimizations across a microgrid the underlying distribution network. Besides, not all without having to share critical data, e.g., marginal proresources would be capable of independent computation duction cost of DERs, available generation capacity, and and communication. metering data, with a third party, such as the DSO cloud. Alternatively, the energy exchange in transactive energy frameworks can be realized among clusters of resources, Above all, MGCs enable microgrids to participate in such as microgrids, which are self-contained in terms of communication and decision making and would have a better Wind chance to be physically connected to peers. Turbine Unique characteristics offered by transactive energy are well aligned with the decenOffice Solar Building tralized management and control required Panel for ADNs and, therefore, can be deployed to enable energy trading among networked microgrids. Figure 7 illustrates a transacResidential tive energy setting for two connected EV House microgrids. Accordingly, there is a microgrid controller (MGC) associated HVAC ESS with each microgrid for monitoring and System control purposes. In other words, an MGC Two-Way is a server that is located at the edge of Diesel Data Communication Generator each microgrid and is capable of performing edge computations. Consequently, technical data gathered from various IoT Figure 6. A representation of a microgrid-wide transactive energy platform. HVAC: heating, ventilation, and air-conditioning. devices throughout a microgrid can be

MGC 1

Bid

Microgrid 1

Microgrid 2 Distribution Utility

Offer

MGC 2

Data Flow Power Flow

Figure 7. The transactive energy setting for two connected microgrids. MGC: microgrid controller.



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elimination or the Jacobian. However, the problem of state estimation is distributed in essence, meaning that each state variable is coupled to other variables through constraints. Thus, state estimation can be solved using an agent-based perspective prevalent in a class of problems Agent-Based State Estimation known as distributed constraint satisfaction problems. Fast computation and communication have been prospects Widespread penetration of intelligent electronic devices of the smart grid paradigm. In line with these promises, various operational applications (IEDs) has encouraged the impleassociated with power systems, mentation of distributed state estiincluding the state estimation, mation where each bus would act have been moving toward decenas an agent receiving asynchroMGCs enable tralization. State estimation aims nous messages from the neighbormicrogrids to to assign values to system states ing agents. Accordingly, the IEDs participate in based on imperfect and redundant at each bus can act as an edge measurements by optimizing a node, providing computation and transactive energy specific statistical criterion. In the communication resources in the markets through local case of power systems, state varivicinity where measurements are ables are the bus voltage magniactually made [7]. decision making tudes at all buses and the relative and by sharing phase angles at all buses but one. Fault Location only noncritical Reaching a precise estimate of Distribution systems are protected the state values is essential for from various classes of faults information, i.e., price maintaining the system security through a set of protective devicand power signals, and monitoring the transmission es, such as fuses, relays, and system through power flows and reclosers, that are coordinated to with peers. voltage limits. locate the faulty section and, furBased on these values, operather, to provide the exact location tors can detect a potential issue of the fault within the preidentiand take corrective actions in a timely manner. The tradified section. However, the considerable adoption of distional state estimation problem can be categorized as a tributed generation (DG) technologies including RERs in weighted least-square minimization. Measurements read legacy distribution systems is reshaping single-source and radial systems to connected microgrids, a network of from different locations of the grid must be stored and multisource and unbalanced systems. Furthermore, the solved centrally using methods such as Gaussian operating authorities do not have control over the installation of DGs. Hence, they can be connected to the distribution system at any arbitrary location, such as the substation, feeders, or even the consumer end. With the ongoing shifts in distriSophisticated bution systems, traditional protection Cloud Layer Fault Location schemes need to be revised to remain effective in finding and isolating the faulted sections of the distribution system in a timely manner. Edge computing has been fused with protection fra meworks not only to Edge Layer Basic Fault overcome the challenges posed by DG Location and Islanding i nt eg r a t ion , but t o m i n i m i z e t he response and outage time as well as 5G 4G the economic loss in fault occurrences. Edge-based fault location does not Zero-Mode Device Layer require synchronization, high-frequenTransient cy sampling, or knowledge of the preCalculation cise wave velocity—characteristics that are often demanded in traditional fault location methods such as the impedance Figure 8. An edge-based fault location architecture. transactive energy markets through local decision making and by sharing only noncritical information, i.e., price and power signals, with peers.

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technique and the traveling-wave approach. Instead, amplitudes of the lower frequency component can be used. Figure 8 depicts an edge-based architecture for fault location in a distribution system with a high penetration of DGs. Accordingly, in case of a fault incident, the zero-mode transients are sampled at the device layer and then passed to the adjacent edge node, where lower frequency amplitudes are obtained. This procedure allows the fault section to be located and, hence, rapidly isolated. The precise location of the fault can be determined at the substation cloud using the amplitude ratio of the two lower frequency components at the fault section. In addition to the above-mentioned advantages, edge-integrated platforms are more immune to cyberattacks and, hence, can handle processes even in the case of partial breakdowns. This is absolutely strategic for critical applications such as fault location [8]. Fog/Edge Computing Along the lines of edge computing, the fog computing paradigm was proposed by Cisco in 2012 to offer cloud capabilities in the proximity of network edges, i.e., network gateways. Since its introduction, industry has massively invested in this concept; however, edge computing was solely embraced by academia as a research topic for years. As an instance, a number of giant industrial groups, including but not limited to Cisco, Dell, Microsoft, and Intel, partnered to found the OpenFog Consortium, which has been collaborating with reputable standard organizations, such as the European Telecommunications Standards Institute and IEEE, to accelerate the implementation of fog computing architectures. The market of fog computing is assumed to mainly impact the five sectors of energy, transportation, industry, agriculture, and health care. The overlap between fog and edge concepts has led to their interchangeable use in the literature. However, we would like to clarify the existing distinctions to highlight the functionalities of each concept and their individual contributions in complementing the cloud paradigm. Both fog computing and edge computing enable intelligent decision making and associated tasks in the vicinity of the network edge. While edge computing often takes place at the first point connected to IoT devices, fog computing occurs one to two hops farther than edge computing. Furthermore, edge computing features more energy and resource limitations as the growing IoT applications create competition over the available resources. Despite the subtle differences, the two concepts are established on the same basis of security, scalability, reliability, agility, and autonomy, which has triggered the idea of integrating these two paradigms as fog/edge computing. Similar to their individual integration with cloud computing, the fog/edge paradigm tends to unburden the cloud by providing services with lower latency and jitter.

Fog/Edge Computing Applications in a Smart Grid Pursuing the same path as edge computing, fog/edge paradigms deployed in the smart grid domain aim to facilitate the smooth integration of DERs in smart grids and encourage the decentralized organization provisioned for these cyberphysical systems. However, the augmented resources available in the fog layer can further extend the applications or considerably improve the functionality, privacy, security, and the quality of service for both end users and operators. In this section, we discuss the fields in smart grids where fog/ edge architectures can have beneficial impacts. Smart Buildings As discussed earlier in this article, in the near future, smart grids will transform into a group of connected yet autonomous entities, e.g., microgrids, operating in a coordinated manner. As automation technologies advance and DERs take over the grid, the current role of microgrids will be extended to smaller self-contained units, e.g., nanogrids, as the building blocks for future smart grids. Accounting for roughly 40% of the total energy consumption in urban areas, smart buildings are viable candidates to perform as nanogrids. Accordingly, the energy consumption optimization within a smart building is of significant importance. Moreover, until the realization of the nanogrid paradigm, sensitive information of residents must be shared with an MGC or the DSO for various social, economic, and technical purposes. Load management and forecasting, real-time monitoring, network planning, and determining the electricity price are examples of applications demanding consumer data. Adversaries can access the fine-grained data on the energy consumption of consumers, which can relay their absence or presence, daily activities, and appliance use at any time and can impair communication and compromise meters. To address these concerns, it has been proposed to release an aggregate value of energy consumption within an area, such as a building. However, it’s not guaranteed that the aforementioned vulnerabilities would not occur, particularly when daily aggregate values are disclosed. Fog computing can be utilized to perform private aggregation on data streaming from smart meters. Meanwhile, some architectures take the privacy measures further and add another privacy layer where edge nodes distribute noise on smart meters so that just an aggregate value can be derived, without disclosure of the exact metering values. In addition to anonymization and aggregation of data, fog nodes can be used besides edge devices to perform local optimizations, e.g., energy cost minimization or preventive maintenance planning based on the preference of residents for each unit within a building. They can then relay the local decisions to an upstream entity, such as a cloud, for managing global optimizations. Blockchain-Based Smart Energy Contracts Subsequent to the major triumph of blockchain as a cybersecure ledger for recording cryptocurrency transactions, Ju ly 2022

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automated and peer-to-peer energy exchange among networked microgrids envisioned in transactive energy frameworks. Accordingly, each MGC in an ADN would have access to a cryptographic record of the previous energy transactions among MGCs, which can be synchronized globally. Moreover, deploying blockchain technology as the underlying platform for energy transactions can effectively discourage the dishonest behavior of MGCs, fur ther enhancing the security and reliability of the entire power grid [9]. Blockchain capabilities have flourished in smart contracts over and above other applications. Smart contracts are, by nature, self-executable scripts that take effect in distributed transactions based on a predefined logic arranged and accepted by all parties. Since smart contracts sit on blockchains as the basis, parties have access to a verifiable cryptographic record of contract execution to trace transactions. Thus, microgrids can trade energy through smart contracts to reach a high level of autonomy, flexibility, and cybersecurity, knowing that other microgrids should respect established rules and regulations. Figure 10 illustrates a potential fog/edge architecture enabling a transactive energy framework based on smart contracts. Accordingly, DERs available at each microgrid, which reside at the edge level of the architecture, would send their generation/consumption schedules as data blocks to the associated MGC. Please note that, for simplicity, in Figure 10 only the DERs of microgrid 1 are illustrated. Based on smart contract 1, MGC 1 optimizes the utilization of associated DERs considering their operational constraints and sends them back to the (a) (b) DERs. Cybersecurity at this level is critical as information protection is often not available for DERs. At the Figure 9. The data flow of ledgers in (a) a centralized framework and (b) a blockchain. fog layer, each MGC participating in the peer-to-peer energy trading declares its preset rules for energy exchange through an individual smart contract to which other MGCs can have access. The bids and Smart Contract 3 offers of microgrids are calculated Smart through individual smart contracts at DSO Cloud Layer Contract 2 this level considering the optimal operWAN Smart ation schedule of the associated DERs. Fog Layer Contract 1 Thus, if the conditions of the MGCs’ LAN MGC 3 smart contracts are satisfied in a transMGC 2 Edge Layer action, negotiations pertaining to the MGC 1 energy exchange will be terminated, and the trading results will be published. DER 1 DER 5 When MGCs make energy deals, the DSO as a nonprofit authority coordinates and DER 2 DER 4 approves transactions to ensure that DER 3 they are aligned with the optimal power WAN: Wide Area Network LAN: Local Area Network flow of the distribution grid and can maintain its secure operation. Thus, the DSO submits trading adjustment messagFigure 10. A transactive energy framework for an ADN based on smart contracts. es to each MGC through another smart

this technology has been introduced to other disparate application domains, including but not limited to supply chain management, digital health care, and document registration. A blockchain can be regarded as a decentralized database that maintains a growing list of data in a chronological order. Each member of the blockchain holds an individual record of data which he or she shares with other members as well. Blockchains are updated only when all members have validated new blocks of data. Accordingly, a blockchain can be regarded as a decentralized database that enables peer-to-peer transactions and communication among anonymous parties, without the supervision of a trusted central authority. Figure 9 compares the validation of a ledger when a central authority is imagined in a database with the point-to-point communication of members in a blockchain. Storing data based on a Merkle tree structure and the associated hash encryption method makes this framework tamperproof. The inherent distributed nature associated with the blockchain technology along with the verifiable and immutable storage of transaction records are the driving forces needed to catalyze

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contract. In highly automated distribution systems, the DSO might change the network configuration in harmony with the optimal power flow to accommodate peer-to-peer transactions. The revision process will continue to ensure that the network security is maintained by the transactions made. Subsequently, the final layer, i.e., the smart contract, will be executed.

Accordingly, fog/edge frameworks can provide optimized cryptography strategies for real-time substation communication while satisfying the privacy and geographical distribution associated with these systems [10].

Fog/Edge Computing Open Issues Compared with cloud computing, the fog/edge paradigm is still nascent, evolving and adapting to the dynamic demand associated with different application domains. Real-Time Substation Communication While some of the existing challenges are specific to a Broad-spectrum cyberattacks, ranging from Sandworm to special field, others may rise in all fog/edge architecDragonfly attacks, have targeted substation networks, partures. Fog/edge architectures are developed based on ticularly smart substations, and have compromised the the vertical interaction of cloud, security of power delivery as well fog, and edge, alongside the horias the physical infrastructure. Accordingly, along with adding zontal interaction of fog nodes. intelligence to critical hubs such as Thus, despite the improved resilDeploying blockchain power substations, the security ience within fog/edge architectechnology as architectures should be revised tures, appropriate interfaces are and transformed to meet emerging required for service provisioning. the underlying requirements. Substations normalAdditionally, the proximity of fog platform for energy ly have a hierarchical architecture nodes to edge devices makes them transactions can of station layer, bay layer, and prosuitable alternatives for performcess layer. The station layer coming local processing and storage effectively discourage prises workstations, a GPS, and services. With the increasing numthe dishonest the regional terminal units (RTUs) ber of connected devices in fog/ to communicate with the remote edge architectures, dynamic taskbehavior of MGCs, control center. The bay layer management algorithms are fundafurther enhancing accommodates mainly the metermental for coordinating services ing devices, protection devices, among different fog nodes. Lastly, the security and and other IEDs. Finally, the mergin fog/edge architectures, both fog reliability of the entire nodes and edge devices are being ing units and intelligent devices power grid. utilized in static and mobile reside at the process layer. In such modes. The mobility of nodes and common architectures, the cyberdevices would create further comattacks occur mainly between the munication complexities. Accordstation layer and the bay layer ingly, competent simulation environments should be through unrecognized laptops and USBs. Thus, virtual prideveloped to realize the efficient integration of fog nodes vate networks and gateway firewalls cannot protect the and edge devices into IoT systems. substation network falsified firmware and infected devices. Encryption and authentication can considerably mitigate attacks as they mostly arise from network traffic. Quantum Computing However, it should be noted that a portion of attacks will In parallel with the introduction of cloud, edge, and fog occur by unauthorized operators. notions to afford efficient and dynamic computation Cryptography can effectively contribute to improved recourses, another line of research tends to focus on innocybersecurity within a substation network. Fog/edge-intevative computation mechanisms rather than optimizing grated schemes can enable cryptography architectures to the operation of resources for performing a particular meet the stringent and real-time requirements of various task. Quantum computing can be categorized among such services provided at a substation network. Accordingly, the research topics as a viable tool with escalated computacloud layer would provide remote, yet global, management tional potentials. of cryptography at various substations. The fog nodes can In classic computations, data are translated into a be embedded in RTUs at individual substations to collect series of binary units called bits, which can take values of only 1 or 0. Subsequently, bit digits are fed to a myriad traffic data for choosing and applying the most effective of transistors of an integrated circuit for further processcryptography algorithm. The cloud layer and the edge layer, ing. However, quantum computing relies on qubits for to some extent, might be able to perform cryptographic data calculations. Accordingly, in addition to the values 1 algorithms. However, the former violates the real-time comand 0, qubits can have an additional state that takes the munication, and the latter lacks the global perspective on values of 0 and 1 simultaneously. While a bit can be traffic information to provide adaptive cryptography.

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regarded as a particular state of a qubit, the aforementioned attribute of qubits can considerably expand the number of simultaneous calculations that can be handled through quantum computing. Undoubtedly, the classic computation rules of bits cannot serve qubits. Instead, principles of quantum physics govern processes in quantum computing. Superposition, entanglement, tunneling, and annealing are a number of quantum mechanisms used to further accelerate calculations. There exists a tremendous potential for integrating quantum computing in the transforming landscape of power system studies, where the IoT has heightened the need for analyzing data. Accordingly, instead of solving an approximation or reformulation of a problem, the exact problem can be handled to reach a solution within an acceptable time scale. Consider a power grid security analysis as an instance that is conventionally performed by examining contingency scenarios, mainly component failures, and requires processing power flow optimization problems for each scenario and analyzing the system state. Because of the lack of processing facilities, only N – 1 scenarios, which include the failure of one component, are studied. However, quantum computing enables a parallel computation of larger numbers of scenarios to assess outages of multiple components as well. Correspondingly, quantum computing can contribute to other demanding problems associated with power systems, such as the ac optimal power flow, contingency analysis, and transient stability [11].

About the Authors Fargol Nematkhah ([email protected]) is with the School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1988914313, Iran. Farrokh Aminifar ([email protected]) is an associate professor with the School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1988914313, Iran. He is a Senior Member of IEEE. Mohammad Shahidehpour ([email protected]) is the director of the Robert W. Galvin Center for Electricity Innovation at the Illinois Institute of Technology, Chicago, Illinois, 60616, USA. He is a Fellow of IEEE and an elected member of the U.S. National Academy of Engineering. Sasan Mokhtari ([email protected]) is the CEO and president of Open Access Technology International, Minneapolis, Minnesota, 55418, USA. He is a Fellow of IEEE and an elected member of the U.S. National Academy of Engineering. References [1] D. Schoder, “Introduction to the Internet of Things,” in Internet of Things A to Z: Technologies and Applications, Q. Hassan, Ed. New York, NY, USA: Wiley, 2018, pp.1–50. [2] P. Mell and T. Grance, “The NIST definition of cloud computing,” Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, USA, Rep. 800-145, Sep. 2011. [3] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: 10.1109/ JIOT.2016.2579198. [4] Z. Li, M. Shahidehpour, and X. Liu, “Cyber-secure decentralized energy manage-

Summary and Challenges The widespread integration of IoT devices has triggered novel methods for exploiting large volumes of data. Thus, the available processing, networking, and storage facilities are deployed to enable data analytics and intelligent decision making. Operation requirements enforced by various applications have given rise to a range of disruptive technologies, e.g., cloud, edge, and fog computing. Along with the changes occurring in computational methods, power systems are facing new phenomena. The extensive penetration of RERs into the electric grid along with the introduction of new loads, such as EVs, are reshaping the grid, particularly the distribution system, into a network of connected microgrids. In this article, we have discussed cloud, edge, and fog computing technologies and their contributions to realizing the decentralized mechanisms envisioned for smart grids, e.g., transactive energy, blockchain, and smart contract paradigms. Despite the activating potentials offered by new players of the electric grid, e.g., DERs and flexible loads, the integration of computing technologies with the existing power system infrastructure might pose several challenges. They include but are not limited to privacy and security concerns, task management, and interfacing, all of which need to be tackled for the effective and reliable operation of these frameworks. 20

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ment for IoT-enabled active distribution networks,” J. Modern Power Syst. Clean Energy, vol. 6, no. 5, pp. 900–917, Sep. 2018, doi: 10.1007/s40565-018-0425-1. [5] GridWise Transactive Energy Framework Version 1.0. (2015). The GridWise ­Architecture Council, U.S. Department of Energy, Washington, DC, USA. [6] F. Nematkhah, S. Bahrami, F. Aminifar, and J. P. S. Catalão, “Exploiting the potentials of HVAC systems in transactive energy markets,” IEEE Trans. Smart Grid, vol. 12, no. 5, pp. 4039–4048, Sep. 2021, doi: 10.1109/TSG.2021.3078655. [7] K. Saxena and A. R. Abhyankar, “Agent-based distributed computing for power system state estimation,” IEEE Trans. Smart Grid, vol. 11, no. 6, pp. 5193–5202, Nov. 2020, doi: 10.1109/TSG.2020.3006932. [8] N. Peng, R. Liang, G. Wang, P. Sun, C. Chen, and T. Hou, “Edge computing-based fault location in distribution networks by using asynchronous transient amplitudes at limited nodes,” IEEE Trans. Smart Grid, vol. 12, no. 1, pp. 574–588, Jan. 2021, doi: 10.1109/TSG.2020.3009005. [9] M. Shahidehpour, M. Yan, P. Shikhar, S. Bahramirad, and A. Paaso, “Blockchain for peer-to-peer transactive energy trading in networked microgrids: Providing an effective and decentralized strategy,” IEEE Electrific. Mag., vol. 8, no. 4, pp. 80–90, Dec. 2020, doi: 10.1109/MELE.2020.3026444. [10] H. Zhang, B. Qin, T. Tu, Z. Guo, F. Gao, and Q. Wen, “An adaptive encryption-asa-service architecture based on fog computing for real-time substation communications,” IEEE Trans. Ind. Informat., vol. 16, no. 1, pp. 658–668, Jan. 2020, doi: 10.1109/ TII.2019.2948113. [11] R. Eskandarpour, P. Gokhale, A. Khodaei, F. T. Chong, A. Passo, and S. Bahramirad, “Quantum computing for enhancing grid security,” IEEE Trans. Power Syst., vol. 35, no. 5, pp. 4135–4137, Sep. 2020, doi: 10.1109/TPWRS.2020.3004073. 

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Toward Named Data Networking An Approach Based the Internet of Things Cloud With Edge Assistance by Xiaonan Wang and Xinyan Qian

T

he Internet of Things (IoT) aims to improve the quality of human life by delivering collected data efficiently for real-time monitoring. With the increasing complexity of data, it is hard for an individual IoT device to produce information due to the restrictions of visual angles and resources. Digital Object Identifier 10.1109/MSMC.2022.3172826 Date of current version: 15 July 2022

2333-942X/22©2022IEEE



For instance, the camera mounted on the front of a vehicle captures only data from the road ahead; it cannot collect information from the side. The local cloud (LC) is a new communication paradigm where cloud members collaboratively generate data locally by sharing their resources, so integration of the IoT and LC (ITLC) should be an effective way to overcome the resource restriction of an individual device. Named data networking (NDN) is a novel and efficient communication mechanism, and its features are able Ju ly 2022

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to assist in realizing the ITLC and enhancing the efficiency of ITLC-based data delivery. However, NDN has different architectures and features than the ITLC, so it is challenging to exploit NDN to realize ITLC. In this article, we propose an edge-assisted, NDN-based ITLC framework and provide evaluation results that verify its advances.

IoT-Based Communication Models and NDN The IoT typically employs address-centric communication models, such as the Internet Protocol, to deliver data [10], but it is hard to utilize address-centric models to realize the ITLC. First, the IoT consists of a large population of mobile devices, and each IoT node needs to be configured with a unique address for proper communication, incurring considerable address configuration costs and delays and degrading data delivery performance. In Overview some scenarios where IoT nodes are multiple hops from With the ubiquity of wireless connections and the rapid dynamic host configuration protocol ser vers, duplidevelopment of smart devices, such as smart phones, the cate address detection (DAD) is employed to guaranIoT has become a reality [1], [2]. The IoT can be applied tee address uniqueness. This further exacerbates costs in all walks of life, including smart health care and safe and delays because DAD is impledriving [3], [4]. For example, nodes mented through flooding. Second, attached to patients are capable of the ITLC is composed of a group sensing vital signs for real-time The Internet of Things of dynamic members, but it is monitoring, and smart phones are difficult to bind a set of dynamic able to capture road safety inforaims to improve the members to an address, and conmation for secure and efficient quality of human sequently, an address cannot be driving. The IoT aims to rapidly used to seek dynamic ITLC memdeliver captured data and achieve life by delivering bers. Third, data are delivered efficient information monitoring collected data between a source and destination. [5], [6]. However, with the increasefficiently for realThis means that a source node ing diversification and complexity can retrieve data only from a desof data, an individual IoT device time monitoring. tination node. However, the goal cannot generate information, of the ITLC is that data can be owing to the restrictions of visual obtained from any optimal ITLC angles and resources, and a novel member. Moreover, this commu n ication pa rad ig m data communication model is required to overcome leads to data delivery inefficiency because 1) the desresource limitations [7], [8]. tination node may not be an optimal provider, and 2) The LC is a new communication paradigm in which sevin cases where the target node is unable to provide eral cloud members share their resources and collaborathe data, the delivery fails even if other nodes can tively generate data locally [9]. Devices can obtain data provide the information. from any optimal cloud member, which greatly shortens To improve the efficiency of data delivery, a novel data delivery delays and enhances success rates. Hence, data-centric communication mechanism, NDN [11], is the ITLC should be an effective way to overcome the proposed. NDN defines three roles: consumers that resource restrictions of an individual device. The IoT is launch data delivery processes to retrieve information, address-centric, whereas the ITLC is data-centric. Therecontent routers that perform forwarding functions, and fore, the IoT needs a novel data-centric communication providers that supply data, as shown in Figure 1. A conmechanism to implement the ITLC. NDN [11] is a typical sumer seeks potential providers by sending interest and a data-centric communication paradigm, so we are motivatname. If the content routers receiving interest have no ed to exploit it to realize the ITLC. However, NDN has a pending interest table (PIT) entry for the name, they credifferent architecture than the ITLC, so it is challenging to ate one PIT entry to establish the reverse path and utilize achieve an NDN-based ITLC. a forwarding information base (FIB) to send interest to Taking into account these challenges, we present an potential providers. After a provider receives interest, it NDN-based ITLC framework that introduces edge computreturns data with the target information to the consuming to assist in implementing NDN. The framework has the ers along the reverse path. following contributions: To summarize, NDN has the following characteristics: ◆◆ The IoT is integrated with the LC to overcome the resource limitations of a single IoT device. ◆◆ Neither consumers nor providers need unique addresses. ◆◆ The framework analyzes the limitations of IoT-based ◆◆ Names are exploited to discover potential providers. communication models and the advantages of NDN and ◆◆ Data can be acquired from any optimal provider. implements the NDN-based ITLC with edge assistance. ◆◆ In-network caching is enabled to shorten distances among data and consumers. ◆◆ The open challenges of the edge-assisted, NDN-based These features should assist in realizing the ITLC and ITLC are discussed, and solutions are proposed, impleenhancing the efficiency of ITLC-based data delivery. mented, and evaluated to verify the feasibility. 22

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Comparisons of characteristics between the IoT and NDN are in Table 1. Edge-Assisted, NDN-Based ITLC In NDN, content routers are stationary and have abundant storage resources, so they are able to perform in-network caching and shorten distances among data and consumers. This is also one of the advantages of NDN. By contrast, IoT devices have limited caching capabilities, so they have difficulty fulfilling in-network caching, especially in scenarios where a large number of them are involved in producing significant amounts of data. Moreover, the mobility of IoT devices leads to frequent changes in locations, so it is hard to guarantee that in-network caching can effectively reduce distances among data and consumers. Compared with resource-constrained IoT devices, edge devices are fixed and have relatively abundant caching resources [12], so they can be integrated with the NDNbased ITLC and assume the role of content routers to help implement in-network caching and reduce distances among data and consumers.

edge devices with relatively abundant resources that are in charge of performing in-network caching and maintaining data delivered by the backbone layer. Routing In NDN, content routers are fixed and have powerful computing capabilities to rapidly create and update the FIB. Moreover, providers, such as servers, are static, and their population is relatively steady. Based on these features, NDN uses flooding to manage the FIB and ensure its validity. In the ITLC, mobile IoT devices have limited processing resources and assume the role of content routers. Because a large number of IoT devices are involved in forwarding and maintaining the FIB, creating and updating the FIB by using flooding may result in broadcast storms, as demonstrated in Figure 3(a). In addition, ITLC members work as providers, and the fact that IoT devices frequently join and leave the ITLC increases the frequency of FIB updates, further deteriorating the situation and ultimately leading to FIB staleness and

Open Challenges and Possible Solutions Although edge devices can assist in realizing in-network caching, it remains challenging to achieve the NDN-based ITLC, due to the different features and architectures of the ITLC and NDN, as detailed in Table 2. Architecture In NDN, stationary content routers provide reverse paths, and static servers work as providers [11], so the FIB and reverse paths are relatively stable. These are prerequisites for performing NDN. By contrast, in the NDN-based ITLC, an enormous number of mobile IoT devices assume the role of content routers and perform forwarding functions. Meanwhile, IoT devices work as providers of data. Consequently, the mobility of IoT devices leads to unstable network topologies, causing a stale FIB and broken reverse paths and ultimately resulting in the failure of data delivery. According to [13], network stability is mainly impacted by the node population involved in forwarding and link performance among mobile nodes. Hence, one possible solution for mitigating the effect of IoT device mobility on network stability is to construct an IoT backbone by electing forwarders to enhance link performance and reduce the IoT device population involved in maintaining the FIB and reverse paths. The architecture of the edge-assisted, NDNbased ITLC is presented in Figure 2. It contains three layers, namely, the ITLC layer, backbone layer, and edge layer. The ITLC layer is composed of IoT devices that cooperate to produce data by sharing resources. The backbone layer consists of forwarders that are elected based on metrics, such as link durations, and are responsible for managing the FIB and reverse paths and delivering data generated by the ITLC layer to the edge layer for caching. The edge layer includes

Interest Data

Interest Data

Figure 1. The data-centric NDN mechanism.

Table 1. The comparison of characteristics. Model

Characteristics

Results

Addresscentric IoT

• Each IoT device is assigned a unique address. • An address is used to identify and discover a specific node. • A source node can acquire data only from a destination node, which might not be optimal.

• Address configuration for each IoT device incurs considerable costs and delays, degrading data delivery performance. • An address cannot be used to seek members because it cannot be bound to a set of dynamic members. • Data cannot be acquired from any optimal member.

Data-centric NDN

• Neither consumers nor providers need addresses. • Names are employed to discover optimal providers. • Data can be obtained from any optimal provider. • In-network caching is enabled.

• Addressing costs and delays are avoided. • A name may be bound to a group of dynamic ITLC members. • ITLC members assume the role of providers, so data can be obtained from any optimal ITLC member. • Distances among data and consumers are shortened.

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Table 2. The open challenges and possible solutions. Challenge

NDN

ITLC

Architecture

• Stationary content routers comprise reverse paths. • Providers are fixed.

• An enormous number of mobile IoT devices assume the role of content routers and perform forwarding functions. • Mobile IoT devices work as providers.

• The mobility of IoT devices leads to network instability. • Network instability results in a stale FIB and broken reverse paths, leading to failures of data delivery.

• Construct an IoT backbone by electing forwarders. • Improve link performance among forwarders. • Reduce the IoT device population involved in forwarding.

Routing

• Flooding is used to manage the FIB and ensure its validity.

• A large number of IoT devices maintain the FIB. • ITLC members work as providers. • IoT devices frequently join and leave the ITLC.

• Creating and updating the FIB by using flooding may result in broadcast storms. • Frequent FIB updates exacerbate FIB staleness, leading to failures of data delivery.

• Reduce the node population involved in forwarding and managing the FIB. • Employ unicast to create and update the FIB. • Update the FIB within a small area.

Mobility

• NDN lacks effective consumer and provider mobility support strategies. • Reverse paths are composed of fixed content routers.

• IoT devices work as both consumers and providers. • IoT devices are mobile and have restricted storage capabilities. • An enormous number of IoT devices comprise reverse paths.

• The mobility of consum• Update the FIB in a timely way ers may result in failures to within a small area. receive data. • Re-establish reverse paths to • The mobility of providers may consumers that change lead to failures to provide data. locations. • Reverse paths become •  Fix disconnected reverse paths. frequently broken.

Data delivery

• Content routers forward interest from each target FIB entry. • Each provider that receives interest sends data back to consumers.

• An enormous number of IoT devices are involved in managing the FIB. • IoT devices may dynamically become providers by joining the ITLC. • A large number of IoT devices comprise reverse paths.

• The number of IoT devices involved in forwarding interest and data grows, resulting in data redundancy. • The number of target FIB entries and number of providers returning data greatly grow, exacerbating data redundancy.

data delivery failure. Reducing the node population involved in forwarding and managing the FIB is an effective way to suppress broadcast storms caused by creating and updating the FIB. Also, unicast may be employed to manage the FIB and help avoid broadcast storms, as in Figure 3(b). To prevent FIB staleness caused by IoT devices dynamically

Edge

Backbone

ITLC

Figure 2. The edge-assisted, NDN-based ITLC. 24

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Results

Possible solutions

• Choose optimal forwarders to decrease both the number of IoT devices forwarding interest and the number of providers returning data. • Exploit data sharing among consumers to reduce the frequency of consumers sending interest and IoT devices forwarding interest.

joining and leaving the ITLC, FIB updates may be controlled within a small area, such as a subnet or domain. Mobility NDN lacks effective consumer and provider mobility support strategies. In NDN, if a consumer fails to obtain data due to its mobility, it has to relaunch a delivery procedure to retrieve information. Although in-network caching shortens distances among data and consumers, the costs and delays caused by restarting an information delivery process are not trivial. Moreover, in NDN, providers, such as servers, are static [11], so provider mobility support is not addressed. However, in the ITLC, mobile IoT devices work as both consumers and providers. Although edge devices are introduced to perform in-network caching, they are deployed at the edge of the IoT and are usually multiple hops from IoT devices. Hence, if the ITLC does not support consumer and provider mobility, it may suffer from the following problems: ◆◆ The mobility of consumers may result in failures to receive data. ◆◆ The mobility of providers may lead to failures to provide data.

This article aims to achieve the ITLC by exploiting Furthermore, in NDN, fixed content routers are NDN. Comparisons among this work and the works [8], involved in reverse paths, whereas in the ITLC, mobile IoT [12], and [14]–[17] are given as follows. devices comprise reverse paths. Inevitably, the mobility of IoT devices causes frequent disruptions of reverse paths, 1) The LC concepts are different: The work in [14] presultimately leading to frequent failures of data delivery, as ents a local automation cloud that is intended to illustrated in Figure 4(a). Hence, the consumer and properform automation functionalities, but it does not vider mobility issues in the NDN-based ITLC and the explain how to implement the local ­automation cloud. reverse-path disruption issue require further research. In [12], the IoT is integrated with a remote cloud comUpdating the FIB in a timely way within a small area, such posed of static devices with rich resources, such as a subnet or domain, should be a solution for provider as servers, rather than the LC composed of mobile mobility. The consumer mobility issue may be solved by re-establishing reverse paths to consumers that change locations, and the reverse path interruption issue may be addressed by fixing broken Interest Interest reverse paths, as shown in Figure 4(b). Data Data Interest

Data Communication In NDN, content routers forward interest from each target FIB entry and build reverse paths to consumers. Each provider receiving interest sends data back to consumers along reverse paths. In this way, consumers can receive data from optimal providers because the FIB and reverse routes are relatively stable. In the ITLC, IoT devices work as content routers, and ITLC members assume the role of providers. Since an enormous number of IoT devices are involved in maintaining the FIB, the number of IoT devices participating in forwarding interest greatly grows, as depicted in Figure 5(a). This results in substantial data redundancy. Moreover, IoT devices dynamically joining the ITLC leads to a dramatic increase in both the number of target FIB entries and the number of providers returning data, further exacerbating redundancy. Furthermore, a large number of IoT devices comprise reverse paths, so the number of IoT devices involved in forwarding data greatly grows. This further deteriorates data redundancy and results in considerable information delivery costs and latency. To reduce data redundancy and alleviate costs and delays, optimal forwarders should be elected using metrics to decrease both the number of IoT devices forwarding interest and the number of providers returning data. Also, data sharing may assist in reducing the frequency of consumers sending interest and IoT devices forwarding interest and in enhancing the efficiency of ITLC-based data delivery, as indicated in Figure 5(b).

Data Interest Data

Interest

Interest

Interest

Data Interest Data

Data

Data

(a)

(b)

Figure 3. The (a) routing challenge and (b) possible solution.

Interest

Interest Data (a)

Interest

Interest Data

Data

Data

(b) Figure 4. The (a) mobility challenge and (b) possible solution.

Interest Data

Interest Data

Interest

Interest

Data

Data

Interest

Interest Data

Interest Data Interest Data

Data

Interest Data

Data (a)

(b)

Figure 5. The (a) communication challenge and (b) solution.



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Implementation, Analysis, and Evaluation The cost C ITLC, delay D ITLC, and success rate S ITLC of ITLC-based data delivery are analyzed, as shown in (1)– (3), where nC is the consumer population, dj is the distance from the jth consumer to the forwarder returning data, c is the cost of transmitting a message between neighbor forwarders, tj is the delay of the jth consumer sharing data, pj is the probability of the jth consumer leaving the associated forwarder, fj is the probability of failing to re-establish the path from the serving forwarder of the jth consumer to the previous forwarder, n RV is the forwarder population involved in reverse paths, qi is the probability of the ith forwarder leaving reverse paths, and ki is the probability of failing to fix the broken reverse path caused by the ith forwarder:

Delay (ms)

Cost

j=1 nC



D ITLC = MAX t j /n C, (2) j=1



S ITLC = % (1 - p j $ f j) $ % (1 - q i $ k i) . (3)

15

2

3

4

5

2

nC

j=1 nC

nC

nC



D STD = MAX m j /n C; MAX t j # MAX m j, (5) j=1 j=1 j=1



S STD = % (1 - p j) $ % (1 - q i); n RV # n RVl . (6)

nC

n RV'

j=1

i=1

Based on (1) and (4), CITLC is compared with CSTD, as shown in (7) and (8): nC

nC

j=1

j=1

C ITLC - C STD = 2 | d j $ c/n C - 2 | s j $ c/n C, (7)



d j # s j & C ITLC # C STD . (8)

Based on (2) and (5), DITLC is compared with DSTD, as shown in (9) and (10): nC

nC



D ITLC - D STD = MAX t j /n C - MAX m j /n C, (9) j=1 j=1



MAX t j # MAX m j & D ITLC # D STD . (10) j=1 j=1

nC

nC

Based on (3) and (6), SITLC is compared with SSTD, as shown in (11) and (12):

4

5

Consumer Population (nC)

Consumer Population (nC)

(a)

(b)

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i=1

C STD = 2 | s j $ c/n C; d j # s j, (4)



Standard

Figure 6. The (a) data delivery costs, (b) delays, and (c) success rates. 26

j=1

C

3

ITLC

n RV

C

65 0

nC

This framework is compared with the standard NDN [11]. The cost CSTD, delay DSTD, and success rate SSTD are shown in (4)–(6), where sj is the distance from the jth consumer to the node returning data and tj is the delay of the jth consumer acquiring data. In the standard, each node performs forwarding, and in this framework, only forwardn ers fulfill forwarding, so s j are not smaller j and MAXm j=1 n than dj and MAX t j, respectively: j=1

130

30

0

nC

C ITLC = 2 | d j $ c/n C, (1)



Success Rate (%)

devices with limited resources, such as IoT nodes. In this article, the LC concept is that IoT devices with limited resources collaboratively produce data by sharing their resources, and the main goal of the LC is to overcome the resource limitations of a single IoT node. Moreover, this article achieves the ITLC by exploiting NDN. 2) The system architectures are different: The works [15]– [17] are based on a single-hop architecture, where IoT devices directly link with edge devices, so the authors do not discuss the routing problem for a multiple-hop IoT. By contrast, this article is based on a multiple-hop architecture, where IoT devices are multiple hops from edge devices, and proposes a hierarchical layer architecture to solve the routing problem for the multiple-hop IoT. 3) The mobility support strategies are different: The works [8], [15], and [16] do not address mobility issues. The work in [17] discusses only possible solutions to consumer and provider mobility issues, and it does not explain how to implement the resolutions. 4) The data delivery methods are different: The work in [8] cannot perform in-network caching, due to the limited storage resources of IoT devices. To overcome this, this article introduces edge computing to perform in-network caching.

100 75 50

2

3 4 v (km/h) (c)

5

nC





References

n RV

S ITLC - S STD = % (1 - p j $ f j) $ % (1 - q i $ k i) j=1

i=1

nC

n RV

j=1

i=1

- % (1 - p j) $ % (1 - q i),

[1] A. Celesti, A. Galletta, L. Carnevale, M. Fazio, A. L´ay-Ekuakille, and M. Villari, “An

 (11)

0 1 f j 1 1 _b b 0 1 k i 1 1 ` & S STD 1 S ITLC . (12) n RV # n RVl ba

IoT cloud system for traffic monitoring and vehicular accidents prevention based on mobile sensor data processing,” IEEE Sensors J., vol. 18, no. 12, pp. 4795–4802, Jun. 2018, doi: 10.1109/JSEN.2017.2777786. [2] H. A. Khattak, H. Farman, B. Jan, and I. U. Din, “Toward integrating vehicular clouds with IoT for smart city services,” IEEE Netw., vol. 33, no. 2, pp. 65–71, Mar./Apr. 2019, doi: 10.1109/MNET.2019.1800236. [3] L. N. Balico, A. A. F. Loureiro, E. F. Nakamura, R. S. Barreto, R. W. Pazzi, and H. A.

To verify the feasibility of the framework, the architecture is implemented and evaluated in the NDN Simulator, which is Network Simulator 3 based. In the simulation environment, nC ranges from two to five, velocity v ranges from 2 to 5 km/h, the communication radius is 40 m, and the media access control protocol is IEEE 802.11. As shown in Figure 6(a) and (b), with the growth in nC, the data delivery costs and delays in the ITLC and standard reduce. The main reason is that the growth in nC shortens the length of reverse paths among consumers and nodes returning data. As detailed in Figure 6(c), with the increase in v, the success rates of data delivery in the ITLC and standard decrease. The primary reason is that the growth in v increases the probability that reverse paths are interrupted and that consumers fail to receive data. Figure 6 demonstrates that the cost and delay in the ITLC are lower than those in the standard and that the success rate is higher. There are two main reasons, as follows. 1) In the ITLC, only forwarders are involved in reverse paths, while in the standard, each node is allowed to join reverse paths. Consequently, the reverse paths in the ITLC are shorter and more stable. 2) The ITLC supports node mobility, so data can be successfully retrieved despite node mobility. In contrast, the standard does not address the node mobility issue, so node mobility may lead to failures of data delivery.

B. F. Oliveira, “Localization prediction in vehicular ad hoc networks,” IEEE Commun. Surveys Tuts., vol. 20, no. 4, pp. 2784–2803, 2018, doi: 10.1109/COMST.2018.2841901. [4] X. Wang and S. Cai, “Secure healthcare monitoring framework integrating NDNbased IoT with edge cloud,” Future Gen. Comput. Syst., vol. 112, pp. 320–329, Jun. 2020, doi: 10.1016/j.future.2020.05.042. [5] S. Arshad, M. A. Azam, M. H. Rehmani, and J. Loo, “Recent advances in information-centric networking-based Internet of Things (ICN-IoT),” IEEE Internet Things J., vol. 6, no. 2, pp. 2128–2158, Apr. 2019, doi: 10.1109/JIOT.2018.2873343. [6] H. Khelifi, S. Luo, B. Nour, H. Moungla, Y. Faheem, and R. Hussain, “Named data networking in vehicular ad hoc networks: State-of-the-art and challenges,” IEEE Commun. Surveys Tuts., vol. 22, no. 1, pp. 320–351, Jan. 2019, doi: 10.1109/COMST. 2019.2894816. [7] E. Lee, E. Lee, M. Gerla, and S. Y. Oh, “Vehicular cloud networking: Architecture and design principles,” IEEE Commun. Mag., vol. 52, no. 2, pp. 148–155, Feb. 2014, doi: 10.1109/MCOM.2014.6736756. [8] X. Wang and S. Cai, “An efficient named data networking based IoT cloud framework,” IEEE Internet Things J., vol. 7, no. 4, pp. 3453–3461, Apr. 2020, doi: 10.1109/ JIOT.2020.2971009. [9] A. U. R. Khan, M. Othman, S. A. Madani, and S. U. Khan, “A survey of mobile cloud computing application models,” IEEE Commun. Surveys Tuts., vol. 16, no. 1, pp. 393–413, 2014, doi: 10.1109/SURV.2013.062613.00160. [10] X. Wang and Y. Lu, “Efficient forwarding and data acquisition in NDN-based MANET,” IEEE Trans. Mobile Comput., vol. 21, no. 2, pp. 530–539, Feb. 2022, doi: 10.1109/TMC.2020.3012483. [11] B. Wissingh, C. Wood, A. Afanasyev, L. Zhang, D. Oran, and C. Tschudin, “Information-centric networking (ICN): Content-centric networking (CCNx) and named data

Conclusion In this article, we discussed the challenges of realizing an NDN-based ITLC and proposed an edge-assisted, NDN-based ITLC framework to overcome these difficulties. The main idea behind the framework is to exploit edge computing to help achieve NDN in the ITLC. Experimental results demonstrate that the in-network caching and data sharing in NDN effectively alleviate the costs and delays of ITLC-based data delivery and that consumer and provider mobility support effectively improve success rates. In future work, we are going to study the security and privacy of the proposed framework and apply the architecture in early warning fields, such as road safety to prevent accidents by achieving rapid ITLC-based data delivery.

networking (NDN) terminology,” RFC 8793, 2020. [12] T. Yu, X. Wang, and J. Hu, “A fast hierarchical physical topology update scheme for edge-cloud collaborative IoT systems,” IEEE/ACM Trans. Netw., vol. 29, no. 5, pp. 2254–2266, Oct. 2021, doi: 10.1109/TNET.2021.3085031. [13] C. Cooper, D. Franklin, M. Ros, F. Safaei, and M. Abolhasan, “A comparative survey of VANET clustering techniques,” IEEE Commun. Surveys Tuts., vol. 19, no. 1, pp. 657–681, 2017, doi: 10.1109/COMST.2016.2611524. [14] J. Delsing, J. Eliasson, J. van Deventer, H. Derhamy, and P. Varga, “Enabling IoT automation using local clouds,” in Proc. IEEE 3rd World Forum Internet of Things (WF-IoT), 2016, pp. 502–507, doi: 10.1109/WF-IoT.2016.7845474. [15] M. Amadeo, G. Ruggeri, C. Campolo, and A. Molinaro, “IoT services allocation at the edge via named data networking: From optimal bounds to practical design,” IEEE Trans. Netw. Service Manage., vol. 16, no. 2, pp. 661–674, Jun. 2019, doi: 10.1109/ TNSM.2019.2900274. [16] Y. Yu, D. Belazzougui, C. Qian, and Q. Zhang, “A concise forwarding information base for scalable and fast name lookups,” in Proc. IEEE 25th Int. Conf. Netw. Protocols

About the Authors Xiaonan Wang ([email protected]) is with the Changshu Institute of Technology, Changshu, China. Xinyan Qian ([email protected]) is with the Changshu Institute of Technology, Changshu, China.

(ICNP), 2017, pp. 1–10, doi: 10.1109/ICNP.2017.8117530. [17] B. Nour et al., “Internet of Things mobility over information-centric/named-data networking,” IEEE Internet Comput., vol. 24, no. 1, pp. 14–24, 2020, doi: 10.1109/ MIC.2019.2963187. 

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The Need for Quantification of Uncertainty in Artificial Intelligence for Clinical Data Analysis Increasing the Level of Trust in the Decision-Making Process

D

ifferent terms such as trust, certainty, and uncertainty are of great importance in the real world and play a critical role in artificial intelligence (AI) applications. The implied assumption is that the level of trust in AI can be measured in different ways. This principle can be achieved by distinguishing uncertainties in predicting AI methods used in medical studies. Hence, it is necessary to propose effective uncertainty quantification (UQ) and measurement methods to have trustworthy AI (TAI) clinical decision support systems (CDSSs). In this study, we present practical guidelines for developing and using UQ methods while applying various AI techniques for medical data analysis.

Digital Object Identifier 10.1109/MSMC.2022.3150144 Date of current version: 15 July 2022

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2333-942X/22©2022IEEE

by Moloud Abdar, Abbas Khosravi, Sheikh Mohammed Shariful Islam, U. Rajendra Acharya, and Athanasios V. Vasilakos

©SHUTTERSTOCK.COM/SCHARFSINN

Introduction In recent decades, an emerging phenomenon has entered applied sciences, called AI. In a short time, the extraordinary power of AI [i.e., machine learning (ML) and deep learning (DL)] has solved a wide variety of problems in many areas. Today, AI is used to solve complicated realworld problems in various research domains. AI technologies have been widely used in many critical systems, such as autonomous vehicles (AVs), cybersecurity systems, and many more [1], [2]. However, including different AI techniques in such systems may pose risks [3]. It is not feasible to devise AI-based systems, which know how to appropriately operate in situations that differ outstandingly from what AI systems have seen erstwhile. Consequently, it is pivotal to develop the next generation of AI systems that can perform adequately in known domains and comprehend, demonstrate, and quantify when faced with unknown situations. Among all AI-based technologies, in particular, ML and DL methods have gained tremendous attention in the mechanization of various



processes such as computer vision and image processing [4]–[6], signal processing [7], gesture recognition [8], visual question answering, text analysis and sentiment analysis (SA) [9], zero-shot learning [10], medical data analysis [11], big data analysis [12], and many more. Today, advances in computer science and high-performance computing find AI extensively benefiting the contemporary world, such as with weather forecasting, fraud detection, face recognition, and predicting diseases and cancers [13]. AI methods have been applied to various tasks, both in private and public sectors. The power of AI has attracted considerable attention from researchers to take advantage of its potential capabilities and subtypes. The remarkable efficiency of AI has paved the way for other branches of science and engineering to apply it in their fields extensively. AI helps to build intelligent-based machines that can improve various processes’ performance. The AI comprises a broad range of techniques such as ML, DL, statistical methods, and expert systems, which fundamentally depend on decision rules. This means that only having an outstanding performance of such intelligent methods is not a criterion for selection or approval by medical experts. Comprehensive reviews of previous studies indicate a close relationship between trust and uncertainty, either in user interfaces or information [14], [15]. In contrast, the onset of new technologies such as AI, big data, ML, and DL provide us with potentially significant insights into a deeper understanding of raw data and the extraction of meaningful and valuable information. Many people still do not approve of these innovative technologies. Recent studies have clearly shown that this lack of trust stems from the weakness of these models in dealing with their uncertainties [16], [17]. Trust is a concept with different definitions. It should be noted that some people claim that trust is only between people, so we cannot “trust” machines of any kind. Humans usually trust others who make fewer mistakes. But trusting nonhumans is also a key challenge. But as Smith and Hoffman [18] mentioned, trust can also be designed for machines. One of the most important applications of AI is undoubtedly in medicine to diagnose diseases and cancers. The rise of AI in medical data analysis has attracted the attention of researchers worldwide. Physicians have wanted to predict the disease early for years before it becomes severe. Although attention by clinical organizations is considered thus far, there is still a gap between medical organizations and physicians with automated systems. One of the main reasons for this gap is the lack of trust by medical experts in either Ju ly 2022

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Another important research domain is the application development or the predictions obtained by handcrafted of different natural language processing (NLP), SA, text conventional ML and DL methods [19]. AI has no volunmining, emotion analysis, and so on [24], [25]. The tary authority, and for this reason, medical experts increasing advancement in computer technology has led believe that it cannot be said to have a character or to many texts with various subjects. NLP should ultimotive. Moreover, so-called black boxes of different AI mately exploit meaning (called technologies, such as handcraftsemantics) from the text. In addied conventional ML and DL methtion, AI methods have demonod s, a re a not her sig n i f ic a nt strated promising results in text reason why medical experts do The remarkable analysis [26]. Along with all these not trust AI-based medical sysefficiency of AI applications, there are several tems [19]. Hence, medical experts has paved the way other important applications of AI are interested in understanding methods; for example, handcraftthe performance of handcrafted for other branches ed conventional ML and DL demconventional ML and DL methods of science and onstrated stunning performance under uncertainty. in the area of weather forecasting One vital strategy to increase engineering to apply [27], online marketing [28], enertrust in these models is applying it in their fields gy-related studies [29], and natuuncer tainty measurement and ral disaster modeling [30]. Finally, quantification methods. The growextensively. business analytics has signifiing importance of the application cantly exploited the use of AI of AI-based techniques is visible. methods [31]. Such intelligent But more important, uncertainty techniques have inspired business people and developers measurement and quantification of these AI methods in to create AI models to yield accurate decisions. various applications have become a key issue in recent years. However, this perspective highlights only the main important reasons for uncertainty measurement and The Era of ML/DL for Medical Data Analysis quantification of ML and DL methods used in medical Ever since its inception, AI has been widely used in many data analysis. applications. Today, many automated systems are proposed to detect and predict various diseases accurately Applications of ML/DL in the health-care domain (e.g., breast cancer [32], prostate cancer [33], liver cancer [34], cancer cells [35], cardiovascular disease [36], COVID-19 [37], and so on). Nonmedical Applications Recent statistics reveal that in the United States, approxiIn this section, we discuss nonmedical and medical applimately only 5% of outpatients receive the wrong diagnocations and briefly present important AI methods in sis yearly [38]–[40]. However, these diagnostic errors are real applications. As mentioned earlier, AI methods are not limited to outpatients but are common, especially used in various applications. For example, researchers when detecting/diagnosing patients with severe medical and countries have highly regarded climate change as a conditions. Accordingly, estimates show that approxisignificant change in regional or global climate patmately 20% of patients with serious medical conditions terns. To predict and track noticeable climate changes, are misdiagnosed at primary care levels [41]. As a result AI methods have been used significantly [21]. The of these errors, one out of three misdiagnoses can cause results reveal the critical effects of these methods to serious harm to patients, leading to harmful clinical conprevent climate cha nge or reduce its destr uctive sequences [42]. effects. But AI applications are not limited to controlThese statistics indicate a large number of errors in ling climate change. Self-driving car (also called AV or medical diagnoses. This reveals that errors may even driver-less car) technology is another critical and sensioccur in physicians’ diagnoses. Therefore, alternative tive application of AI methods. To secure the route of a solutions are strongly recommended to reduce these self-driving car from the first point A to target point B, errors. Evidence shows that medical experts foresee AI divers use AI methods [22]. On the other hand, driver demonstrating significant potential in diagnosing, manbehavior analysis can be listed as one of the essential aging, and treating a wide range of medical conditions. research domains to improve the quality of transportaHowever, there are numerous obstacles to using AItion and reduce the number of deaths caused by accibased methods in daily medical and clinical practice, dents. Hence, monitoring drivers’ behavior seems to be particularly concerning the regulation of these emergof great importance. Determining risky driving behaving technologies. iors helps to improve road safety. Thus, AI methods It should be noted that to reduce the risk of using such have indicated promising achievements in detecting AI methods used for medical data analysis and decision risky driving behaviors [23]. 30

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On the other hand, uncertainty can cause an error making, different health agencies, such as the U.S. Food in decision processes. Therefore, awareness of cerand Drug Administration, have strict regulatory requiretainty or uncertainty in the decision-making process ments for licensing these medical systems [43]. This meaningfully impacts achieving desired results. In means that there is increasing awareness of the importhe data science resea rch tance of regulatory bodies to mondoma i n, t he deci sion-ma k i ng itor AI-based medical devices. process that is concerned with Monitoring these agencies can In the data science how t o choose t he opt i m a l help increase trust in intelligent model or method is often conmedical systems, but it still does research domain, sidered nonrational [46]. Nevernot lead to complete confidence. the decision-making theless, it remains an important Despite the distrust from meddecision on choosing the optiical experts, the importance of process that is mal model, input data, number AI methods in various branches concerned with how of samples and factors, and so of medicine cannot be ignored, on. Given these conditions, how including from data storage and to choose the optimal such deci sion s c a n be m a de extraction of useful knowledge model or method u nder u ncer t a i nt y shou ld be from raw medical data. Maintainis often considered out l i ned. Hence, qu a nt i f y i ng ing da ily patient in for mation uncertainty in handcrafted conleads to the emergence of extennonrational. ventional ML and DL is imporsive data. Undoubtedly, these big, tant and can improve decision raw data contain useful informam a k i n g by ex po si n g we a ktion, which can be used in reality nes ses and strengths of the underlying models and [44]. Traditional or manual methods are incapable of data sources [47]. accurately storing or analyzing this volume of data, Trust is the main foundation of societies, sciences, which is increasing every day. One of the drawbacks of sustainable development, economies, and so forth. TAI is conventional techniques developed for health science is based on the idea that the entire potential of AI can be that they have concentrated too much on comparing achieved when TAI can realize trust in all stages, includeffectiveness while spending much less or too little ing the development, deployment, and use of AI-based on the primary needs of actual individuals [45]. The systems. The effects of trust are quite evident in reality in possible blame can lie with outdated epidemiological a way; distrust of a case (i.e., any real example) can preand statistical tools, suggesting reducing relevance to vent it from moving further forward. For example, supthe primary needs of simultaneous clinical decision pose neither physicians nor their patients trust an m a k i n g . Va r iou s A I me t ho d s h a ve e m p owe r e d AI-based system to diagnose diseases or dispense treatresearchers by providing more effective and faster frameworks to analyze big medical data. The major ment recommendations. In that case, it is unlikely that aim of using such AI methods is to understand differanyone will consider its recommendations seriously, even ent disease mechanisms. if those treatments or recommendations can increase patients’ well-being. It is always important to develop What Is Uncertainty? trustworthy, accurate, and robust AI-based systems In almost all life scenarios, we are often confronted employed in various applications. with two words when making a decision: certainty and uncertainty [17], as Increasing Knowledge Decreasing Knowledge show n in Figure 1. People a re conRisk stantly engaged in making decisions in various cases in everyday life. Having certainty or uncertainty is inevitable in rea lity, a nd we face them in Absolute Uncertainty Absolute Certainty different situations, especially when making decisions. Indeed, the decision-ma k i ng process is enti rely Awareness Ignorance focu sed on how con f ident we a re Probabilities and Some Knowledge of Probabilities and regarding a particular decision being Expected Outcomes Probabilities and Expected Outcomes a correct (good) decision or an incorare Known Expected Outcomes are Unknown rect (bad) one. This means that decision making with certainty can lead to Figure 1. Certainty versus uncertainty during decision making and obtaining better results. their relationship with knowledge and risk [94].

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Most of the AI-based methods are still black boxes, Trust and distrust are mainly considered during recand humans usually cannot understand the main cause ommendation and decision-making processes, in which of decisions and/or predictions. As mentioned earlier, uncertainty is the boundary line between (their schematic good performance by a broad range of handcrafted conrelationship is drawn in Figure 2). In other words, it can ventional ML and DL methods alone is not enough. They be said that there is an obvious relationship between trust should define their credibility and uncertainty. This highlights and confidence in their results that we rarely precisely know the (predictions). As Doshi-Velez and exact boundaries of our guaranKim [52] expressed, the main goal teed knowledge under uncertainty, Explainability of humans is to acquire knowlso we can entirely trust in our is the ability to edge from a variety of sources. daily judgments within its range. However, humans are often incaHence, it can be argued that uncerexplain further pable of providing a precise and tainty indicates a lack of authentic what influenced the complet e way of ex pre s si ng knowledge concer ning future predictions made knowledge. Therefore, the best events. This means that uncertainapproach is to ask for explanaty in our knowledge can reduce by a wide variety of tions to turn into knowledge. trust. For these reasons, we must ML- and DL-based Moreover, almost all the end-todevelop and expand AI-based sysend AI models are never completetems that consider uncertainties systems. ly testable as one cannot make an and offer accurate solutions to entire list of scenarios in which deal with them. Thus, based on models may fail [52]. Consequently, Figure 2, it could be argued that we need explainable AI models to infer from where and facing the probability of gain with more certainty can why the predictions/decisions came. Explainable and interlead to having trust, whereas having the probability of pretable AI models are always more trustable. To achieve gaining with more uncertainty can lead to distrust. this, the confidence of various handcrafted conventional One of the most important components is explainabiliML and DL methods can be evaluated using different UQ ty in AI, particularly in the handcrafted conventional ML techniques. Using this approach, when the confidence in and DL methods. Explainability is the ability to explain prediction is low (uncertain), implement fallbacks or the further what influenced the predictions made by a wide system can be reimplemented. variety of ML- and DL-based systems. Miller [48] comprehensively studied explainability in AI and listed two complementary ways to increase transparency and trust Deal With Uncertainty of various intelligent agents: 1) generating decisions To deal with various uncertainties during training and (also called explainability or interpretability) and 2) predictions using AI methods, a significant number of UQ explicitly explaining decisions (also called explanatechniques have been introduced for various applications tion). On the other hand, Wang et al. [49] clearly [17]. A very comprehensive overview of UQ procedures in explained an apparent link between input uncertainty conventional handcrafted ML and DL methods can be awareness and trust in explanations. Moreover, several found in [17]. studies have highlighted the importance of the causability and explainability of AI-based systems in medicine UQ in ML/DL [50] and drug discovery [51]. As Marsh et al. [15] mentioned, trust has a significant role in various disciplines, such as AI applications, data science, decision making, and many more. On the other hand, applying a wide variety of handcrafted conventional ML/DL methods depends heavily on different facTrust Distrust tors, such as data types and their complexity, ML/DL algorithms, and structures. In handcrafted conventional ML/DL modeling, UQ is needed to improve the trustworthiness, predictability, and validity of AI methods applications. Indeed, most handcrafted conventional Probability Probability ML and DL methods have yielded outstanding predicof Gain of Loss tive accuracy with poor uncertainty estimators. In other words, presenting predictive distribution uncerUncertainty tainty plays an essential role in AI applications, representing the confidence level of AI methods concerning Figure 2. Trust, distrust, and their relationship with their predictions. uncertainty [95]. 32

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Generally, uncertainty can be categorized into two main groups: epistemic and aleatoric uncertainty [53], [54]. Hullermeier and Waegeman [55] have comprehensively studied both aleatoric and epistemic uncertainties. Epistemic uncertainty (also known as systematic) relates to uncertainty occurring due to variability in the model parameters for a specific data set. This uncertainty can be mitigated by using more data. This type of uncertainty is absent in a real production environment because the deployment stage usually covers the choice of a single solution, i.e., the model that yields the maximum posterior probability for a given data set. The second category is aleatoric uncertainty (also known as statistical), which relates to the inherent uncertainty in the data. This category of uncertainty cannot be mitigated by applying more data. Therefore, it can be said that aleatoric uncertainty measures noise, which is a fundamental uncertainty, while unlike aleatoric, epistemic is a reducible uncertainty. Figure 3 represents a schematic view of different uncertainties occurring in handcrafted conventional ML and DL during medical image classification. As indicated in Figure 3, medical predictions should be rationalized and given various circumstances by adding an extra layer to explore uncertainty. Given the uncertainty of their predictive predictions, the assessment of handcrafted conventional ML- and DL-based CDSSs provide compliance with professional principles of medical outcome analysis. This can help CDSSs refrain from giving predictions with high uncertainty or show places in data and models where uncertainty is high.

Therefore, UQ can deal with some concerns, such as bias and robustness of the proposed new techniques resulting from the preliminary design of these new technologies. Consequently, it can be concluded that having methods that deal with uncertainty and increasing the level of trust of clinical groups can solve technical challenges. Thus, we require an actionable approach to formulate these uncertainties. Uncertainty is an obvious candidate, either for accepting or rejecting while making predictions. As a result, we might refrain from providing an answer (a choice) when predictions are very uncertain [54]. There are various rejection functions, even applying uncertainty as to the rejector. The prime disadvantage of this technique is that it is essential to train the rejector with the classification method and requires admission to the model’s internals. Although actionable uncertainty is a path to pursuing algorithmic accuracy accountability, this becomes a setback when one is not in control of all components and/or steps in the data product. Recently, large applications based on DL made the reinterpretation of prevailing techniques, including dropout or stochastic mechanisms such as Monte Carlo strategies [56], broaden the utilization of such procedures to address uncertainty in DL models. Therefore, applying various UQ methods enhances the quality of decisions. Uncertainties in ML/DL Using a wide variety of AI methods can lead to having automated decision-making processes. As clearly discussed in [15], the possibility of explaining how an automated decision is made to users may reduce their

Decision Boundary Aleatoric Uncertainty

Epistemic Uncertainty (a)

(c) (a)

(b)

(c)

(d)

Malignant

(b)

Benign (d) Label Uncertainty Out-of-Distribution Sample Figure 3. A schematic view of different uncertainties that can occur in handcrafted conventional ML- and

DL-based medical image classification (pictured is an example of skin cancer): (a) benign, (b) malignant, (c) malignant, and (d) radiation burn [96].



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uncertainty. However, there is not yet a comprehensive study to support this claim. Recent studies have clearly shown the inherent uncertainty in handcrafted conventional ML- and DL-based predictions and its close relationship with public trust in science [57]. It relies on the fact that decision making can gain our trust when accompanied by the least uncertainty. For example, one of the fundamental criticisms related to the accuracy of handcrafted conventional ML and DL models presented for COVID-19 detection is that these models ignore considering their uncertainty. In other words, the weakness of AIbased models emerges as they cannot clearly show the uncertainties or propose a solution to deal with them neither in the early stage of model making nor in the stage of making predictions. Therefore, treating and quantifying uncertainty can be considered a form of transparency [58]. As displayed in Figure 2, there is a small boundary between trust and mistrust related to uncertainty. Accordingly, this can be generalized to ML and DL methods. If various proposed ML and DL methods cannot pay attention to these uncertainties in their model and its predictions, it can lead to distrust. But perhaps it can be argued that this issue is becoming much more prominent in the medical world. Trust in the predictions and recommendations of ML and DL models becomes more questionable unless the uncer tainty is taken into account. This is important because of the close relationship of these predictions and recommendations with patients’ lives. This makes perfect sense as physicians prefer not to take risks when people’s lives are in danger. This is the main point at which the uncertainty measurement and quantification techniques help increase the level of trust in how these methods are developed

and the obtained results by considering the lack of knowledge when developing any ML and DL model or reporting the results (predictions). Therefore, UQ and estimates ca n help people make better decisions because individuals may not correctly understand uncertainty or may not have advanced skills [58]. Therefore, in ML and DL, we use the term uncertainty to refer to humans’ lack of knowledge regarding some important results of interest [58].

The Need to Quantify Uncertainties in ML/DL for Medical Data Analysis When discussing trust in medicine, the importance of trust in the decision-making process becomes more serious. Trust-based decisions are critical for medical data analysis and for different applications. For this reason, decisions must be made with certainty or with the least uncertainties. An accurate clinical diagnosis is a critical task that can refrain from an overconfident, incorrect quantification. This allows clinicians and medical experts to execute subsequent revisions in cases with high uncertainties [59]. Dealing with high uncertainty (or low confidence) in handcrafted conventional ML and DL methods regarding their medical data predictions plays a significant role in having more reliable machine-based models. As an ideal goal, we prepare models for expressions “I do not know” or “I am not sure.” This seems to be a fundamental necessity for the safe deployment of such AI-based medical systems. This gives an important capacity to refrain from providing a diagnosis or prediction during uncertainty. And this results in having a second opinion to avoid having unpredictable outcomes. As depicted in Figure 4, the model needs to make a prediction anyway, even when it is not sure of the results. This problem appears in most classic handcrafted conventional Model Training ML/DL approaches. In other words, many AI methods show outstanding perforMalignant DL mance and have significant weaknesses in measuring uncertainties. One solution P (Malignant) to deal with such problems is to apply UQ OR methods. The models encompass both the P (Benign) Input Layer Hidden Layer Output Layer objective and subjective information Begin derived from clinical data and clinical experiences, respectively [60]. It can be Model Testing DL noted that exclusively data-driven models P (Malignant) = 0.2 (i.e., ML and DL medical data analysis Unseen models) have extremely limited applicability as they overlooked the clinical P (Benign) = 0.8 Input Layer Hidden Layer Output Layer experience. The various models used in medicine continually approximate differP is the Prediction of the Model ent realities, but most of them do not consider reality itself. Figure 4. A normal prediction procedure for seen and unseen These models are often dependent on samples. This figure shows a DL model used for skin cancer binary the state of current knowledge about a set classification. 34

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influence the final prediction. Based on this evidence, it of decisions and cannot extend their knowledge to the can be argued that clinical experts’ distrust of these subject. Although models can make decisions, this canmodels stems from this point of view. not be a final decision because the models must be In recent decades, various handcrafted conventional revised using the available new information. This means ML- and DL-based CDSSs have been proposed to examine that they have a certain range of knowledge already specimedical decisions and quantify uncertainty accordingly. fied for them. This point can be considered a boundary These methods can bring transparency to the results point between certainty and uncertainty in AI models (it obtained and subsequently facilitate clinical decisions. The probably applies to many other models too). In other words, point to consider is that clinicians and other specialists different AI models are constantly developed in particular need not be experts in mathematics, statistics, or even circumstances, and slight changes in those conditions can engineering to use them. This shows that CDSSs can be significantly affect behavior of the models. As illustrated in developed, expanded, and placed alongside physicians and Figure 2, the major partition between trust and distrust is a clinicians to serve patients. But we line that can have an exemplary also believe that uncertainties impact on decision-making processshould be considered when develes: uncertainty. Proposing unceroping and making predictions tainty estimation and quantification It can be concluded using AI-based CDSSs. Handling approaches with handcrafted conthat having methods any uncertainty in CDSSs can lead ventional ML and DL methods can to more physicians trusting these provide a better confidence level that deal with systems enough to use them fretoward the obtained results by uncertainty and quently. We must emphasize that those techniques. increasing the level of the results obtained by AI-based Uncertainty is a central and CDSSs methods, either with or significant factor in all medical trust of clinical groups without UQ, do not mean that the decision-making processes and can solve technical results are definitive. Hence, clariassists with all important medification should be made about AIcal decisions in highly uncertain challenges. based CDSSs and UQ approaches. environments. These processes Quantifying the uncertainties of AI are entirely stochastic. At the methods does not mean that they same time, the parameters used can be arbitrary and independent systems. In other words, to describe these processes should also be interpreted researchers active in handcrafted ML and DL development as random variables [60]. Fortunately, it is possible to try to propose assistive systems in clinical decision-makidentify random variables and incorporate them into AI ing processes. Therefore, it can be concluded that the promodels. Among the available modeling techniques, simposed models are more of an assistive tool for clinicians. ulation methods offer a general approach to the widest However, the more these assistants are aware of the points interpolation of uncertainties in stochastic environwhere uncertainty occurs, the more accurate decisionments. Numerous stochastic techniques can deal with making process becomes. There is a strong need to apply various uncertainties in such stochastic environments. and pay special attention to uncertainty estimation and These stochastic methods have been used in various quantification methods during medical data analysis. fields, including medical subjects. But it should be noted that nonstochastic methods have also been used to Discussion quantify uncertainties, but we avoided comparing and In the following sections, we discuss a few most impordetailing stochastic and nonstochastic approaches with tant challenges and research gaps for the application of UQ. The reader is referred to [17] for a comprehensive UQ AI methods. In this regard, in the “The Problem of review study on UQ methods in AI procedures. Access to Codes and Medical Data” section, we first bring In medical data analysis, uncertainty can be considup the most common issue in AI research: access to ered feedback for a clinical diagnosis. This means that codes and medical data. Thereafter, the “Future Orientauncertainty estimation and quantification can withhold tions” section lists a few of the most important research erroneous, overconfident medical diagnoses and predicdirections for applying UQ methods in medical data analtions. As a result, it allows clinicians and physicians to ysis. Finally, the summary of this article is reported in further manage future reconsiderations and revisions for the “Concise Ultimate Message” section. a few cases with high uncertainty. As discussed earlier, despite dramatic improvement in the performance of AI methods, they have no inherent approach to quantify or The Problem of Access to Codes measure the uncertainty associated with their predicand Medical Data tions. Moreover, they also cannot provide any sign of The access to codes and data from previous studies will which features in the model’s input significantly significantly help improve the results of former studies

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and provide more accurate automated models. Thus, access to data and codes of previous studies can pave the way for future research. This is even more important in medicine because not all researchers have access to real and high-quality data on various diseases. Unfortunately, a review of past work done on various medical topics reveals that access to medical codes and data are almost impossible in most cases. Most likely, the main reason for not sharing medical data may be due to ethical commitments made by medical centers to researchers before sharing the data. Easy access to medical data is essential, for which medical centers must provide a fundamental solution. This will increase access for more active researchers in AI to provide newer, more accurate, and standardized AI-based solutions. Hence, we strongly believe that more public medical databases should be without patients’ personal information. On the other hand, not sharing codes is entirely up to AI researchers. This means that researchers have complete independence to share their codes with others. Nevertheless, the strategic suggestion is to make code sharing more common among individuals after getting final approval for the articles. The point that should not be overlooked is that code sharing among researchers has increased in recent years. This may be due to many reasons, such as requests from various conferences/ journals to publicize data and codes, and ease of access to various websites to share their codes and data (e.g., GitHub and Kaggle). It should be noted, however, that there are many more websites for this, but we have mentioned only two. Consequently, sharing codes and medical databases plays an important role in developing better CDSSs. Future Orientations Although the challenges in using UQ for various AI methods are significant, new research opportunities are also equally important (possibly even more so) and exciting. But it should be noted that reviewing past or ongoing research in the area of UQ is beyond the scope of this work (See [16] and [17] for comprehen sive UQ rev iew paper s.) I n t h i s sect ion, we describe a few important open research directions regarding the application of UQ techniques with AI methods for medical data analysis, which requires the contribution of computer scientists, the data science community, engineers, physicians, the health-care industry, and clinical sectors. ◆◆ Application of generative adversarial networks (GANs) [61]: A lack of access to sufficient medical data is one big problem in medical research. As mentioned earlier, researchers do not have access to sufficient medical data, which may prevent them from developing accurate and robust AI-based systems using different ML methods. GANs are novel generative techniques that create new data sa mples 36

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resembling the training data. These techniques have been widely used for different purposes, such as signal processing [62]; image processing [63], [64]; text analysis [65]; and so on. Along with these applications, GANs have shown outstanding performance in medical applications [66], [67]. There is uncertainty while GAN generates more medical data, which needs to be examined. ◆◆ Application of visual attention mechanisms [68]: Generally, attention mechanisms assist a network in weighing features in terms of the importance of one specified task and then use weights to improve the performance of the model for the given task. The outstanding performance of attention mechanisms on NLP tasks is reported in [69]. However, the outstanding performance of these attention mechanisms is well proven, and the importance of their use in vision and image processing tasks is not negligible [68]. On the other hand, these methods performed well in medical areas [70]–[72]. We believe that choosing the right place of applying attention might be accompanied by uncertainties. This is very important for medical topics because of the location of diseases and cancers. Hence, attention mechanisms need to be chosen more carefully as the associated uncertainty of these schemes negatively affects results. Therefore, UQ methods are excellent techniques for dealing with this challenge. ◆◆ Consideration of continual learning [(CL), also named continual lifelong learning] methods [73]: Throughout their lifespan, humans have a tremendous capacity to acquire, fine-tune, and transfer knowledge and skills continually. It can be said that CL aims to fill the gap between human learning and ML. In other words, CL tries to bring ML closer to humans. Hence, many effective and useful CL methods have been introduced in medicine (for example, see [74]). However, regularization and quantification of uncertainties in CL play a critical role in developing more reliable results when new class data are applied. Recently, uncertainty-regularized CL methods, which yielded promising performance, were introduced [74]–[76]. A few studies have been conducted on the uncertainty of CL applied to medical data [74], [77]. We therefore propose introducing more uncertainty-based CL methods for medical data. ◆◆ Application of multimodality (multimodal) medical data analysis [78]: In multimodal data, data come from multiple sources, which is common in medical data. For example, electronic health records are representative examples of multimodal (multisource) data collections [79]. Combining features from multiple medical sources such as images, signals, and clinica l repor t s (t ex t) help s obt a i n a n a ccu r a t e diagnosis. The uncertainties in predictions increase while working with different multimodal data and

models. Thus, measurement and quantification of uncertainties in multimodal medical data are highly recommended. ◆◆ Application of UQ methods in dynamic AI models [80], [81]: Static models are usually trained offline while dynamic ones are trained online. A notable difference between the two models is that we train the static models once and use those trained models for a while. Unlike static models, a dynamic model is regularly upgraded with new input data. This seems to be a good fit for the medical world. As noted earlier, these changes in the data can increase uncertainties in AI models. Hence, we propose using dynamic UQ methods to analyze medical data as a more practical solution. ◆◆ Application of UQ methods in federated learning (FL) [82], [83]: FL involves training various statistical ML models over different remote devices or siloed database centers, such as hospitals or mobile phones, while keeping data decentralized and localized. This approach can be used for the Internet of Things [82], [84] and networks [85], [86], especially in the healthcare domain [87]. FL has been remarkably investigated in different domains, however, there are very few studies on FL for health-care applications, along with UQ. Therefore, quantification of uncertainties in FL

can play a significant role in developing more reliable results when FL in health care is applied. Concise Ultimate Message It may be noted that there is an inextricable link between trust and uncertainty, and trust will be almost absent when uncertainty emerges. Thus, to increase trust, uncertainties should be examined transparently, and proper solutions for dealing with these uncertainties should be provided. The importance of this issue is far greater in the application of AI methods because of their inability to measure and quantify uncertainties, especially in medical data. As clearly discussed in the previous section, although the application of these techniques in medical data analysis has yielded promising results, it has not yet gained the trust of clinicia ns. We feel that weakness in dealing with the uncertainty of models and an inability to predict accurate classes are the main reasons for the lack of trust. Hence, we emphasize the development of AI-based CDSSs, which are sensitive to their uncertainties. The important point is that although AI methods in medical applications are extremely important, considering the uncertainty of those approaches during predictions is no less valuable. Therefore, it can be noted that uncertainty measurement and quantification can help bring

HER Derived Data Demographics Laboratory Reports Clinical Data Repository

Problems and Diagnoses

CDSS (AI Techniques)

Family History Uncertainty Quantification

Patient

Visits Information Medications

Other Hospital Data Pathology

Appropriate Reliance (Following Correct Suggestions)

Imaging Genomics

Clinician

Self-Reliance (Ignore Correct Suggestions)

Reliability? Trust?

Explanations

Distrust?

Overreliance (Following Incorrect Suggestions)

Figure 5. A general view of applications of UQ methods in clinical decision support systems (CDSSs) [92], [93].



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medical organizations and physicians closer to intelligent systems using various AI techniques [88]–[91]. Figure 5 presents a general view of the application of UQ methods in CDSSs. Conclusion Due to AI methods’ increasing popularity and outstanding performance, it is crucial to provide suitable solutions to raise the confidence level in the obtained results. There is a compelling need to employ UQ procedures and measurement for various AI methods. This is far more important for the medical field because the decision-making process of these schemes directly influences the lives of people. Today, data-driven-based methods are emerging as the main foundation for evidence-based, decision-making techniques. However, important theoretical foundations are not well developed to understand their potential, which has deep sociological and psychological concepts to delegate on machine decisions. Hence, understanding these predictive systems, coupled with data- and model-driven techniques, strongly addresses the uncertainty issues in AI techniques. This seems to be much more sensitive to medical issues because it deals with people’s lives. Hence, there is a great need to have useful, practical methods to deal with model uncertainty and improve the overall confidence toward obtained outcomes. Finally, we emphasize that measuring and quantifying AI methods is needed in all science disciplines, not only for medical data analysis.

Athanasios V. Vasilakos ([email protected], [email protected]) is with the College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China, and the Center for AI Research, the University of Agder, Grimstad, 4879, Norway. References [1] S. Aradi, “Survey of deep reinforcement learning for motion planning of autonomous vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 2, pp. 740–759, Feb. 2022, doi: 10.1109/TITS.2020.3024655. [2] X. Liu et al., “Privacy and security issues in deep learning: A survey,” IEEE Access, vol. 9, pp. 4566–4593, Dec. 2020, doi: 10.1109/ACCESS.2020.3045078. [3] N. Ståhl, G. Falkman, A. Karlsson, and G. Mathiason, “Evaluation of uncertainty quantification in deep learning,” in Proc. Int. Conf. Inf. Process. Manage. Uncertainty Knowledge-Based Syst., Jun. 2020, pp. 556–568. [4] F. Pourpanah et al., “A review of generalized zero-shot learning methods,” 2020, arXiv:2011.08641. [5] X. Wang, Y. Zhao, and F. Pourpanah, “Recent advances in deep learning,” Int. J. Mach. Learning, vol. 11, pp. 747–750, Feb. 2020. [6] D. P. Fan, G. P. Ji, G. Sun, M. M. Cheng, J. Shen, and L. Shao, “Camouflaged object detection,” in Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognit., 2020, pp. 2777–2787, doi: 10.1109/CVPR42600.2020.00285. [7] P. Pławiak and M. Abdar, “Novel methodology for cardiac arrhythmias classification based on long-duration ECG signal fragments analysis,” in Biomedical Signal Processing. Singapore: Springer-Verlag, 2020, pp. 225–272. [8] Y. Zhang, L. Shi, Y. Wu, K. Cheng, J. Cheng, and H. Lu, “Gesture recognition based on deep deformable 3D convolutional neural networks,” Pattern Recognit., vol. 107, p. 107, 416, Nov. 2020, doi: 10.1016/j.patcog.2020.107416. [9] M. E. Basiri, M. Abdar, M. A. Cifci, S. Nemati, and U. R. Acharya, “A novel method

Acknowledgments We thank Prof. Timothy Miller at the University of Melbourne for checking the article and providing valuable feedback and comments. This research was supported in part by the Australian Research Council’s Discovery Projects funding scheme (project DP190102181). The corresponding author is Moloud Abdar.

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About the Authors Moloud Abdar ([email protected]) is with the Institute for Intelligent Systems Research and Innovations, Deakin University, Geelong, 3216, Australia. Abbas Khosravi is with the Institute for Intelligent Systems Research and Innovations, Deakin University, Geelong, 3216, Australia. Sheikh Mohammed Shariful Islam is with the Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, 3125, Australia. U. Rajendra Acharya ([email protected]) is with the Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore, the Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, 599491, Singapore, and the Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan. 38

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Achieving Finite-Time Consensus by G. Narayanan, M. Syed Ali, Jianan Wang, Syeda Asma Kauser, Ahmed A. Zaki Diab, and H.I. Abdul Ghaffar

T

he Takagi–Sugeno (T–S) fuzzy-based impulsive consensus problem of a fractional-order multiagent system (FOMAS) with an average dwell time (ADT) is investigated. FOMASs are nonlinear systems, and they are modeled as linear subsystems using a T–S fuzzy model. We study a T–S fuzzy FOMAS subject to a class of impulse time sequences with the ADT approach. In this article, an impulsive control scheme is proposed to make the tracking error

converge in a finite-time consensus into a small neighborhood of origin. Based on impulsive fractional differential equations theory, the Lyapunov functional approach, and the ADT technique, an impulsive controller is designed to achieve finite-time consensus of a T–S fuzzy FOMAS. Finally, through numerical as well as practical examples, the effectiveness and superiority of the proposed approach are validated. Background The fractional-order system recently became an enticing control strategy, namely as a generalization of the

Digital Object Identifier 10.1109/MSMC.2022.3168994 Date of current version: 15 July 2022

2333-942X/22©2022IEEE

©SHUTTERSTOCK.COM/FOUAD A. SAAD

Impulsive Consensus of Fractional-Order Takagi–Sugeno Fuzzy Multiagent Systems With Average Dwell Time Approach and Its Applications



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integer-order system [1]. The synchronization of chaotic fractional differential systems, following these results, is becoming a challenge because of the potential applications in control and communication processing. In addition, several practical systems, like power system dc–dc converters, electrical circuits, and permanent magnet synchronous motors (PMSMs), can be elegantly represented and accurately modeled using fractional calculus (see [2] and [3]). It is widely known that the consensus of a multiagent system (MAS) is one of the most important phenomena in recent years; thus, it has been extensively studied [4]– [6]. Nonlinear dynamic agents cannot be avoided in a physical system, and they can be used in engineering applications, for example, in robotic manipulators [7]. In control theory, considerable attention has been given to research on the consensus of FOMASs (see, for example, [8] and [9]). The T–S fuzzy model offers a valuable tool for approximating several complex nonlinear systems. The model easily links a variety of linear subsystems with fuzzy membership capabilities. Because of the mathematical simplicity of T–S fuzzy analysis, the problem of control of nonlinear dynamic systems has been investigated extensively [10]. The authors of [11] investigated a T–S fuzzy control problem based on linear matrix inequalities for chaotic systems of fractional order with T–S fuzzy model PMSM applications. Recently, several authors have addressed the issue of consensus of T–S fuzzy MASs [12], [13]. An impulsive control scheme does not need continuous state information and, thus, it is simpler and more robust in real systems [14]–[16]. The strategy of impulsive control for a MAS thus needs to be developed. An impulsive control approach has also been used in MAS consensus by researchers [17], [18]. In addition, in many practical applications, the consensus is important in finite time, especially for control issues. Thus, finite-time coordinations have attracted a significant number of researchers in the field of MAS [19], [20]. Still, a few achievements are based on the consensus problem of a T–S fuzzy FOMAS under an ADT via impulsive control. Based on the previous analysis, we aim to study the leader-following consensus problem of a T–S fuzzy FOMAS via impulsive control using ADT approaches. The key contributions of this study are as follows. The criteria guaranteeing the finite-time consensus are given, and the ADT is estimated. Compared with existing results, the proposed impulsive control strategy in this article requires only local neighboring information. Thus, the proposed impulsive method can reduce the running cost and be applied easily to practical multiagent engineering systems. 1) In [8] and [9], a continuous control scheme has been studied for FOMASs. Compared with those works, our study considers a T–S fuzzy impulsive control scheme, 42

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2022

which can effectively reduce the cost of signal transmission between agents. 2) A suitable Lyapunov functional is constructed in the fractional domain, and, by utilizing fractional calculus theory and algebraic graph theory, sufficient conditions are designed to reach finite-time consensus of a T–S fuzzy FOMAS. An exponential consensus with convergence rate is also realized. 3) Finally, numerical simulations are proposed for a PMSM chaotic model via the T–S fuzzy approach and on Chua’s circuit to exhibit the performance and applicability of the suggested theories. Model Description For the algebraic graph theory and some definitions of fractional calculus, please refer to [1], [4], and [15]. A nonlinear FOMAS with one leader and N followers is described by

C t0

D bt 1 p (t) = g (1 p (t), t) + u p (t), p = 1, 2, f, N, (1)

where Ct D bt refers to the Caputo fractional derivative with 0 1 b 1 1, 1 p = [1 p1, 1 p2, f, 1 pn] T ! R n denotes the information state of agent p, and g : R + # R " R is a nonlinear function. The distributed impulsive controller is designed as 0

u p (t) = | d ) ^t - t k h b k a | a pq ^1 p ^t k h - 1 q ^t k hh 3



k=1

q !Np

+ w p ^1 p ^t k h - 1 0 ^t k hhk, k ! N +,

 (2)

where b k is the impulsive strength. The time sequence {t k} satisfies 0 1 t 0 1 t 1 1 f 1 t k - 1 1 t k 1 f, and lim k " 3 t k = + 3. d ) (t) is the impulsive control function satisfying d ) (t) = 0, for t ! 0. Let w p $ 0 as the weight of the edge from the leader node to node p, w p, is greater than 0 if and only if there is an edge from the leader node to node p, and W = diag{w p} ! R n # n . The FOMAS (1) is defined by T–S fuzzy as follows: Rule l for the agent p; IF i p1 (t) is M l1, f, and i ph (t) is M lh; THEN ZC b ] t D t 1 p (t) = A l 1 p (t), t ! t k, r ] + n l (i p (t)) ]] T1 p (t k) = 1 p (t k ) - 1 p (t k ) = b k | l=1 [ (3) # a | a pq (1 p (t k) - 1 q (t k)) ] q!N ] ] + w p (1 p (t k) - 1 0 (t k)) k, k ! N + . \ 0



p

The defuzzified output of the T–S fuzzy FOMAS under an impulsive controller is represented as

Z r ] Ct D tb 1 p (t) = | n l (i p (t)) A l 1 p (t), t ! t k, ] l=1 r ] T 1 ( ) = b k | n l (i p (t)) a | a pq (1 p (t k) (4) [ p tk l=1 q!N ] ]] - 1 q (t k)) + w p (1 p (t k) - 1 0 (t k)) k, \ 0



p

where k ! N +, and T1 p (t k) is the jump of the state of follower agent p at time instant t k . 1 p (t +k ) = lim t " t 1 p (t), and 1 p (t -k ) = lim t " t 1 p (t). 1 p (t) is left continuous at t = t k; i.e., 1 p (t k) = 1 p (t -k ). We assume that the controller design is based on relative state information. Then we define the synchronization error as follows [17]: + k

Main Results Theorem 1 Under Assumption (A M), for some positive scalars t 1, t 2, {, and h, system (7) can achieve finite-time consensus if the ADT satisfies x a 2 x )a =



k



1t p (t) =

|a

pq

(1 p (t) - 1 q (t)) + w p (1 p (t) - 1 0 (t)), (5)

q !Np

where 1t p (t) = [1t p1 (t), 1t p2 (t), f, 1t pn (t)] T ! R n .

l T ln (1 + (m dmax - 1) E b (m max T b)) , (8) ln { (t 2) - ln h (t 1) - ln E b (m lmax T b)

where m lmax and m dmax are the maximum eigenvalues of (A Tl + A l) and (b k (L + W) + I N ) T (b k (L + W) + I N ) , respectively. c (t) = - R lr= 1 (n l (i p (t)) - n l (i 0 (t))) A l s 0 (t). Proof For system (7), let us find the following candidate for the Lyapunov function: N

V (t) = V (t, s (t)) = | s Tp (t) s p (t). (9)



p=1

The global tracking error for node p ( p = 1, 2, ..., N ) is

s p (t) = 1 p (t) - 1 0 (t), (6)

where s p (t) = [s p1 (t), s p2 (t), f, s pn (t)] ! R n . From (4)–(6), the tracking error system is obtained as

Taking the fractional derivative V (t) with system (7) and according to Kronecker product 7 properties, we have



C t0

r

N

l=1

p=1

D bt V (t, s (t)) # | n l (i p (t)) | s Tp (t)(A Tl + A l) s p (t) # m lmax V (t, s (t)).

r Z ] Ct D bt s p (t) = | n l (i p (t)) A l s p (t) ] l=1 r ]] + | [n l (i p (t)) - n l (i 0 (t))] A l s 0 (t) [ (7) l=1 ] + c (t), t ! t k, p = 1, 2, f, N, r ] ] Ts p (t k) = | n l (i p (t)) b k 1t p (t k), t = t k, l=1 \

When t = t k, k ! N +, one gets

0



N

V (t +k , s (t +k )) = | s Tp (t +k ) s p (t +k ) p=1 r

# | n l (i p (t k))((b k (L + W) 7 I n + I N )) T 



l=1

# ((b k (L + W) 7 I n + I N )) s T (t k) s (t k) d # m max V (t k, s (t k)). (11)

where c (t) is the compensation controller. Definition 1 [19] The system (7) is said to achieve finite-time consensus with respect to (t 1, t 2, t 0, T, g) if 1 p (t 0) - 1 0 (t 0) 1 t 1 & 1 p (t) - 1 0 (t) 1 t 2, p = 1, 2, ..., N, 6t ! (t 0, t 0 + T],

Let us now look at the impulsive fractional-order system according to the comparison principle as follows (please refer to [15]):

where t 1 2 0, t 2 2 0, T 2 0 are constants, and t 1 1 t 2 .



Assumption ( AH) The nonlinear functions g (x p (t)) in system (1) satisfy the Lipschitz condition with a non-negative constant U 2 0; i.e.,

and

g (x 1 (t)) - g (x 2 (t)) # U x 1 (t) - x 2 (t) . Assumption ( A M) (A M1).   m lmax 1 0, and 0 1 m dmax 1 1, for k ! N +, (A M2).   (m dmax - 1) E b (m lmax T b) 1 1/H, where H 2 1.

 (10)



(

C t0

(

C t0

l D bt V (t, s (t)) = m max V (t, s (t)), t ! t k (12) + + d V (t k , s (t k )) = m max V (t k, s (t k)), k ! N +,

D bt j (t, s (t)) = m lmax j (t, s (t)), t ! t k (13) Tj (t +k , s (t +k )) = m dmax j (t k, s (t k)), k ! N + .

According to the lemma in [15] and from (12) and (13), V (t, s (t)) # j (t, s (t)) a n d V (t k, s (t k)) # j (t k, s (t k)); then V (t, s (t)) # j (t, s (t)). Applying the fractional integral operator on both sides of (13), we have Ju ly 2022

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Z l ] j (t 0, s (t 0)) + m max # t (t - p) b - 1 j (p, s (p)) Cb t ] ] # dp t ! [t 0, t 1], k ] d j (t, s (t)) = [ j (t 0, s (t 0)) + (m max - 1) | j (t m, s (t m)) m=1 ] l  t m ] + max # (t - p) b - 1 j (p, s (p)) dp, Cb t ] ] t ! (t k, t k + 1]. (14) \

j (t, s (t)) # j (t 0, s (t 0)) (1 + (m dmax - 1)

0

0

k

d l (t m - t 0) b) + (m max # | E b (m max - 1) 2 m=1

#

l E (m max (t m1 - t 0) b)

l # E b (m max (t m2 - t 0) b)

h

d (m max - 1) k

It follows that (see [24]), for t ! [t 0, t 1], we get

|

 b E b (m max (t m1 - t 0))

1 # m 1 # m 2 ...m k # m l l # E b (m max (t m2 - t 0) b) ...E b (m max (t mk - t 0) b)) l b # E b (m max (t - t 0) ),

j (t, s (t)) # j (t 0, s (t 0)) E b (m lmax (t - t 0) b), which leads to

|

1 # m1 # m2 # k

j (t 1, s (t 1)) # j (t 0, s (t 0)) E b (m lmax (t 1 - t 0) b). (15)

(18)

for t ! (t k, t k + 1], k ! N + . k # n and t - t 0 # T, t ! (t 0, t 0 + T]; then

For t ! (t 1, t 2], from (15), we have d j (t, s (t)) = j (t 0, s (t 0))(1 + (m max - 1) E b (m lmax (t 1 - t 0) b)) l t m (16) + max # (t - p) b - 1 j (p, s (p)) dp. Cb t  0



d l j (t, s (t)) # j (t 0, s (t 0)) (1 + (m max - 1) E b (m max T b)) n  (19) # E b (m lmax T b).

By the definition of ADT in [18], N g (t 0, t 0 + T ) , we obtain

Similar to the idea of (16), it follows that

T

j (t 2, s (t 2)) # j (t 0, s (t 0)) (1 + (m - 1)  # E b (m lmax (t 1 - t 0) b)) E b (m lmax (t 2 - t 0) b). (17) d max



j (t, s (t)) # j (t 0, s (t 0)) (1 + (m dmax - 1) E b (m lmax T b)) x  # E b (m lmax T b). (20) a

Denoting { = min {j (t, s (t))} and h = max {j (t, s (t))}, we can get

In general, we have t ! (t k, t k + 1],



{ "s (t), # j (t, s (t)) and j (t 0, s (t 0)) # h s (t 0) . (21) From (19)–(21), we have

4

2

1

0

{ _ s (t) i # h _ s (t 0) i^1 + ^m dmax - 1 h E b ^m lmax T b hhx # E b ^m lmax T b h,

T a

which implies that 1 p (t) - 1 0 (t) # { -1 7h _ 1 p (t 0) - 1 0 (t 0) i

3



(a)

d l l ^1 + (m max - 1) E b (m max T b) hx # E b (m max T b)@ . (22) T

a

0

3

1

According to (21) and (22), we can obtain t 1 = < 1 p (t 0) - 1 0 (t 0) < . This conflicts with the following inequality of < 1 p (t 0) - 1 0 (t 0) < 1 t 1 . Thus,

2

l 1 p (t) - 1 0 (t) # { -1 6h (t 1) ^1 + ^m dmax - 1 h E b ^m max T b hh x l b (23) # E b (m max T )] . T

d

4

6

5

(b)

From (23) and its ADT satisfying (8), it follows that 7

Figure 1. The communication topology for (a) four

followers and one leader and (b) seven followers and one leader.

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IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2022

1 p (t) - 1 0 (t) 1 { -1 ^{ ^t 2 hh . From Definition 1, < 1 p (t) - 1 0 (t)< 1 t 2 and < 1 p (t 0) - 1 0 (t 0)< 1 t 1, so the finite-time consensus issues of system (7) can be reached.

x a 2 x a) =



j (t, s (t)) #



ln a

1k Y . (24) 1 (m lmax) b

a

1 a(t - t ) e j (t 0, s (t 0)), (28) b 0

From (28) and (29), we have

Here, we take Y = (1 + (m dmax - 1) E b (m lmax (t - t 0) b)) . Equation (25) can be rewritten as j (t, s (t)) # e

. (27)

{ s (t) # j (t, s (t)) and j (t 0, s (t 0)) # h s (t 0) . (29)



t - t0



1 l max) b (t - t 0)

where the convergence rate a = _ ln Y x a + ^m lmax h1 b i 1 0. D e n o t i n g { = min {j (s (t))} and h = max {j (s (t))}, we can get

j (t, s (t)) # ^1 + ^m dmax - 1 h E b ^m lmax ^t - t 0 hb hh x # E b (m lmax (t - t 0) b) j (t 0, s (t 0)) . (25)

ln Y x a (t - t 0)

1 (m e b

From (26) and (27), we obtain

Proof It follows from Theorem 1 that we have

lim E b (m lmax (t - t 0) b) # lim t"3 t"3



Theorem 2 Under Assumption (A M), for given positive scalars z and h, system (7) can achieve exponential consensus with convergence rate a and an ADT satisfying

1 p (t) - 1 0 (t) # M 0 e a(t - t ) 1 p (t 0) - 1 0 (t 0) , 0

E b (m lmax (t - t 0) b) j (t 0, s (t 0)) . (26)

where M 0 = { -1 h b, p = 1, 2, ..., N as t " 3. Since the convergence rate a 1 0, we conclude that system (7) can reach exponential consensus with the ADT satisfying (24).

It follows from the Mittag-Leffler expansion function (see [16]) that we obtain

10 5 p1(t )

0 –5

11 21

–10

31 41

–15

2

4

6

8

10

12

14

16

18

20

10

10

5

5 p3(t )

p2(t )

t (s) (a)

0

0

12

–5

–10

13

–5

22

5

10

15

23

32

33

42

43

20

–10

t (s) (b)

5

10

15

20

t (s) (c)

Figure 2. The consensus error s p for each agent p of system (32) with input up (4), p = 1, 2, 3, 4. (a) The

consensus error s p1 for each agent p of system (32) with input up (4). (b) The consensus error s p2 for each agent p of system (32) with input up (4). (c) The consensus error s p3 for each agent p of system (32) with input up (4).

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Special case: We can also extend to a nonlinear FOMAS (1) under impulsive control (2) without the T–S fuzzy model. The tracking error system is defined as b C t D t s p (t) = Bs p (t) + (g (s p (t), t) - g (s 0 (t), t)), t ! t k, (  Ds p (t k) = b k e p (t k), t = t k (30)  0

where B ! R n # n is defined as a constant matrix. Theorem 3 Under Assumption (A H), for some positive scalars t 1, t 2, {, h, and U, system (30) can achieve finite-time consensus if the ADT satisfies x a 2 x a) =

Table 1. The physical meaning of symbols.

w h e r e m Bmax a n d m fmax a r e t h e l a r ge s t e i g e n v a l u e s o f (B T + B) and (b k (L + W) + I N ) T (b k (L + W) + I N ) , respectively.

i as, i bs

Phase a, b stator current

i as, i bs

Stationary a – b axis stator current

i ds, i qs

Synchronously rotating d–q axis stator current

v as, v bs

Stationary a – b axis stator voltage

v ds, v qs

Synchronously rotating d–q axis stator voltage

vdc

DC-bus voltage

qlr

Rotor flux angle

qm

Mechanical angle

dir

Rotor direction

~r

Rotor speed

Ta, Tb, Tc

Phase a–b–c duty cycle ratio of PWM signal

PI

∗ vqs

v∗

ds

Numerical Simulations In this section, we provide numerical examples to e x pl a i n t he b e ne f it s of t he r e s u lt s pr o p o s e d i n t h i s a r t icle.

Ta

∗ vas

PI ids∗

Proof The proof of Theorem 3 is similar to that of Theorem 1.

Example 1 We consider the impulsive control of a FOMAS PMSM ch a ot ic model. Some m a nu fa ct u r i ng i ndu st r ie s, including paper textile mills, are looking at PMSMs that can be modeled as MASs [3]). In this example, we

PWM: pulsewidth modulation.

∗ iqs

Inverter Park

B + U) T b)) T ln (1 + (m fmax - 1) E b ((m max , (31) B ln { (t 2) - ln h (t 1) - ln E b ((m max + U) T b)

Space Vector Gen.

v∗

bs

PI

Tb

PWM Driver

Tc

PWM1 PWM2 PWM3 PWM4 PWM5 PWM6

qlr ids iqs

Wr

SMOSPD Speed Estimation

ias Park

ibs

ibs

dir

Ileg2_ Bus Driver

ADCIN2 ADCIN3

Encoder

qlr qm

ADCIN1

ias Clarke

QEP_A Ramp Gen.

QEP Driver

Voltage Source Inverter

PMSM

QEP_B QEP_inc

Figure 3. Real-time model-based design of a PMSM. PI: proportional integral; PWM: pulsewidth modulation;

SMOSPD: sliding mode rotor speed estimator; QEP: quadrature encoder pulse; ADC: analog-to-digital converter.

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have a physical experiment on a FOMAS of a PMSM chaotic model with a T–S fuzzy approach with the advantage of proposed impulsive control. The FOMAS of the PMSM chaotic model is described as (please refer to [10] and [11])



*

D bt 1 p1 (t) = - 1 p1 + 1 p2 1 p3, D bt 1 p2 (t) = - 1 p2 - 1 p1 1 p3 + v1 p3, (32) b C t D t 1 p3 (t) = z (1 p2 - 1 p3), C t0

C t0 0

where b = 0.67 and v, z are the parameters. Assume that 1 p1 (t) ! [- d 1, d 1], and 1 p2 (t) ! [- d 2, d 2] . The following def­ ine the T–S fuzzy system model (32): ◆◆ Rule 1: IF 1 p1 is M 1 (1 p1), THEN Ct D bt 1 (t) = A 1 1 (t) . ◆◆ Rule 2: IF 1 p1 is M 2 (1 p1), THEN Ct D bt 1 (t) = A 2 1 (t) . 0

0

◆◆ Rule 3: IF 1 p2 is M 3 (1 p2), THEN ◆◆ Rule 4: IF 1 p2 is M 4 (1 p2), THEN

where 1 (t) R- 1 S S0 S0 T

0 -1 z R- 1 S A3 = S 0 S0 T

=

V 0 W - d 1 + vW, -z W X 0 d 2 VW - 1 v W, z - zW X

D bt 1 (t) = A 3 1 (t) . D bt 1 (t) = A 4 1 (t) . A1 = 61 p1 (t), 1 p2 (t), 1 p3 (t)@T , C t0

C t0

R- 1 0 V 0 S W A 2 = S 0 - 1 - d 1 + vW, S0 z -z W T X V RS 1 0 - d 2W A4 = S 0 - 1 v W. S0 z -zW T X

The membership function of the fuzzy sets can be chosen as M 1 (1 p1) = 1 2 (1 + 1 p1 d 1), M 2 (1 p1) = 1 2 (1 1 p1 d 1), M 3 (1 p2) = 1 2 (1 + 1 p2 d 2), M 4 (1 p2) = 1 2 (1 - 1 p1 / d 2) . We adopt the T–S fuzzy method as the dynamical

Test Level 1: Space Vector Generation Test Level 2: Currents/dc-Bus Voltage Measurement Verification Test Level 3: Tuning of dq-Axis Current Closed Loop Test Level 4: Encoder Verification Test Level 5: Speed Closed Loop

Figure 4. Simulation model-based design of a PMSM.



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system to form an impulsive control of a FOMAS of a PMSM model with four followers and one leader, as shown in Figure 1(a). The parameters are selected as v = 20, z = 4, d 1 = 10, h = 0.5. d 2 = 25, T = 0.010, and b k = - 0.83. According to the criteria in Theorem 1, we choose t 1 = 3.7, t 2 = 12.5, { = 0.8, and By using inequality (8), we obtain x a 2 x a) = 1.0039. Then system (32) under the impulsive control tracking problem can achieve finite-time consensus. From Figure 2, we conclude that finite-time consensus of system (32) with impulsive controller (2) is eventually reached, which shows the efficiency of the impulsive controller design method proposed. Moreover, v is connected to the stator current and rotor magnet’s maximum flow. The damping coefficient, inductance of the stator, resistance of the stator, and moment of inertia are all related to z . All of these physical quantities can be measured, and we can obtain matrices A 1, A 2, A 3, and A 4 from v and z. In a real-time PMSM plant, the fractional-order model is identified according to the experimental tests using a MATLAB Simulink model. The physical meanings of the symbols are listed in Table 1. Model-based designs of a PMSM are shown in Figures 3 and 4.

be found in [23]. The communication topologies are the same as those in Example 1. Chua’s circuit explains the dynamics of each of the follower agents. Let 1 p1 = j 1, 1 p2 = j 2, and 1 p3 = i p3; then the networked system is defined as follows: RC D b V R 0 VWRS1 p1VW S t 1 p1W S- H w H w b C S D t 1 p2W = S H d - H d H l WS1 p2W SC b W S S W D t 1 p3 0 - H p - H kW 1 p3 T X T XT X  - X d f (1 p1) 0 +> H + up. 0



(33)

From (33), the dynamics of agent p with control u p can be written as

C t0

D tb 1 p (t) = B1 p (t) + g (1 p (t)) + u p (t), (34)

R-H H 0 V w S w W S H d -H d H lW, B = where S 0 H HW p k T X g (1 p) = [U i 1 p1 + 1 / 2 (U r - U i) (; 1 p1 + 1 ; - ; 1 p1 - 1 ;), 0, 0] T , H w = 1 / RC 1, H d = 1 / RC 2, H l = 1 / C 2, H p = 1/L, H k = R 0 /L, X d = 1/C 1 . Subsequently, we choose H w = 9.1, H d = 1, H l = 1, H p = 9.1, H k = 16.5811, X d = 0.1380, U i = -1.3938, U r = - 0.7559, b = 0.79, T = 0.031, and b k = - 0.83. According to the criteria in Theorem 3, we select the parameters t 1 = 2.50, 1 p = [1 p1, 1 p2, 1 p3] T ,

Example 2 We consider the following Chua’s circuit (see [22] and [23]), where the descriptions of C 1, C 2, L, j 1, j 2, i 3, R 0, R, and f (j 1) = U i j 1 + 1 / 2 (U r - U i) (; j 1 + 1 ; - ; j 1 - 1 ;) can

3 11

p1(t )

2

21 31 41

1 0 –1

0

0.5

1

t (s) (a)

3

1.5

3 12

13

2

22 32

p3(t )

p2(t )

2

42

1 0 –1

23 33 43

1 0

0

0.5

1 t (s) (b)

1.5

–1

0

0.5

1

1.5

t (s) (c)

Figure 5. The consensus error s p for each agent p of system (34) with input up (4), p = 1, 2, 3, 4. (a) The

consensus error s p1 for each agent p of system (34) with input up (4). (b) The consensus error s p2 for each agent p of system (34) with input up (4). (c) The consensus error s p3 for each agent p of system (34) with input up (4). 48

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2022

4 11 21

2 p1(t )

31

0

41 51

–2 –4

61 71

0

0.1

0.2

0.3

0.4

0.5 t (s) (a)

5

0.6

0.7

0.8

0.9

1

4

12

13

32

0

42 52

–5

23 p3(t )

p2(t )

22

2

33 43 53

0

62 72

0

0.2

0.4

0.6

0.8

1

63

–2

73

0

0.2

t (s) (b)

0.4

0.6

0.8

1

t (s) (c)

Figure 6. The consensus error s p for each agent p of system (30) with input u p, p = 1,..., 7. (a) The consensus

error s p1 for each agent p of system (30) with input up (4). (b) The consensus error s p2 for each agent p of system (30) with input up (4). (c) The consensus error s p3 for each agent p of system (30) with input up (4).

t 2 = 7.53, { = 1.3, and h = 4.7. By solving inequality (31), we can obtain x a 2 x )a = 0.7419. Then nonlinear FOMAS (34) with networks of Chua’s circuits under the impulsive control tracking problem can achieve finite-time consensus. Figure 5 illustrates that finite-time consensus is reached by all of the followers with the leader. Example 3 We consider the FOMAS (30) under impulsive control, in which the communication topology graph G with seven followers and one leader is selected, as in Figure 1(b). The nonlinear function g (1 p (t), t) = 0.2 sin (1 p (t)), and B = I, s a t i s f y A s s u m p t i o n (A H ) w i t h U = 0.4. C h o o s e b = 0.57, T = 0.080, b k = - 0.76, { = 0.5, h = 0.7 t 1 = 1.2, and t 2 = 1.5; from inequality (31) of Theorem 3, we obtain x a 2 x a) = 1.0172. Thus, system (30), under the impulsive control tracking problem, is achieved in finite time. The consensus errors are described in Figure 6, which illustrates that the followers can track the leader system achieved in finite time. One can obser ve from these simulation results that the proposed distributed impulsive control is helpful in achieving finite-time consensus with a faster convergence rate. This result reveals that the developed impulsive controller design is more effective.

Conclusion This article is concerned with the leader-following consensus of T–S fuzzy impulsive consensus control of a FOMAS under an ADT. By employing the theory of impulsive fractional differential equations and the ADT technique, a new sufficient condition is obtained. Furthermore, criteria for finite-time impulsive control protocols have been derived, and the ADT is estimated. Under an impulsive control scheme, we discussed the finite-time extended exponential convergence rate performance of the considered system. The factors of effect on consensus are discussed, including the parameters of impulsive strength. The ADT also can be obtained by calculation. Numerical simulations of the proposed practical significance of the impuls i v e c o n t r ol le d F OM A S we r e p e r fo r m e d , t h e corresponding theoretical criteria were demonstrated, and the design demonstrated good effectiveness. Some works on stability analysis of a variable-time impulsive nonlinear FOMAS with input saturation will be considered in future research. Acknowledgment This project is supported by Science and Engineering Research Board (SERB) project SERB/MATRIX project/2018/000931. Ju ly 2022

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About the Authors G. Narayanan ([email protected]) earned his Ph.D. from Thiruvalluvar University. He is with the Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, 632115, India. His research interests include control systems and cyberphysical systems. M. Syed Ali ([email protected]) earned his Ph.D. from Gandhigram Rural Institute-Deemed University, India. He is with the Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, 632115, India. His research interests include stochastics systems and fuzzy systems. Jianan Wang ([email protected]) earned his Ph.D. from Gandhigram Rural Institute-Deemed university, India. He is with the School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China. His research interests include dynamical systems and networked control systems. Syeda Asma Kauser ([email protected]) earned her Ph.D. from Gandhigram Rural Institute-Deemed University, India. She is with the Department of Mathematics, Prince Sattam bin Abdul Aziz University, Al Kharj, 16273, Saudi Arabia. Her research interests include multi-agent systems and dynamical systems. Ahmed A. Zaki Diab ([email protected]) earned his Ph.D. degree from Novosibirsk State Technical University, Novosibirsk, Russia. He is with the Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt. His research interests include electrical systems and control systems. H.I. Abdul Ghaffar (abdelghafar_ibrahim@yahoo. com) is with the New Urban Communities Authority, New Minia City, Minia, 62511, Egypt. His research interests include electrical systems and renewable energy systems.

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©SHUTTERSTOCK.COM/AYENK ROCNAP

Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression

Simplifying Complex T2F Calculations

by Assef Zare, Afshin Shoeibi, Narges Shafaei Bajestani, Parisa Moridian, ­Roohallah Alizadehsani, Majid Hallaji, and Abbas Khosravi

M

any studies have been performed to handle the uncertainties in the data using ty pe-1 fuzzy regression (FR). Few ty pe-2 fuzzy (T2F) regression studies have used interval type-2 (IT2) for indeterminate modeling using type-1 fuzzy membership.

Digital Object Identifier 10.1109/MSMC.2022.3148569 Date of current version: 15 July 2022

2333-942X/22©2022IEEE



The current article proposes a triangular T2F regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data. The tria ng ula r seconda r y membership fu nction is used instead of widely used interval-type models. In the proposed model, vagueness in primary and secondary fuzzy sets is minimized, and also, a specified x-plane of the observed value is included in the same a- plane of the predicted value. Complex calculations of the T2F Ju ly 2022

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simpler, more comprehensible, and practical models of model are simplified by reducing the 3D T2F set into 2D type-2 models. IT2 fuzzy models. A few researchers have applied T2FLR to model the The current article presents a new regression model of issues [14], [15], [22]–[26]. A T2F qualitative regression T2F by considering the more general form of T2F membertechnique was introduced by Wei and Watada [69]. They ship functions and, thus, avoids high complexity. The peremployed a general T2F number, whereas they employed formance of the developed model is evaluated using the IT2. Poleshchuk et  al. [25] posited a model for IT2FS Taiwan Stock Exchange Capitalization Weighted Stock according to the least-squares Index (TAIEX) and COVID-19 foreapproximation. Hosseinzadeh et al. casting data sets. Our developed [27] proposed a model with certain model reached the highest perforinput and output in accordance T2FS has various mance when compared to the with weighted goal programming other state-of-the-art techniques. applications in (WGP). However, in this model, Our developed method is ready to electrical energy, only a few points of fuzzy funcbe tested with more uncertain data tions converge. and has the potential to predict the business and Few studies have focused on weather and stock values. finance, health the T2F c-regression (T2FCR) models, wh i c h a r e d i f fe r e n t Fuzzy Sets care, automatic from T2FR [28], [29]. None of Real-life information usually has control, and medical vagueness; consequently, Zadeh [1] the previous models are able to applications. introduced the type-1 fuzzy set provide a good model of T2FR, (T1FS) to model this vagueness. a nd a ll of them have reduced T2F systems (T2FSs) were introthe T2FR model to several points duced since T1FS cannot model of t he mem b er s h ip f u nc t ion that information with high-level uncertainty [2]. Despite closer together. An IT2FR model was suggested by the ability of the T2F system in modeling natural phenomShafaei et  al. [30], based on a- cuts concepts, reachena, these models were not considered in the beginning ing good results [31]. due to their high computational complexity. IT2 fuzzy In all previous T2FR models, the secondary membersets (IT2FSs) were employed more than other T2F modship function of T2F membership functions is considered els because of their abilities in modeling phenomena and an interval fuzzy set. To be able to use the T2FS in modelalso their simplicity of calculations. Nevertheless, it is ing uncertainties in a more comprehensive way, it is betnot easy to explain the application of any T2FS. However, ter if the membership functions have a more general T2FS has various applications in electrical energy, busishape. Hence, the triangular secondary membership ness and finance, health care, automatic control, and functions are considered. The present study has modeled medical applications [3]–[9]. the uncertainties of the membership function of a T1FS Finding a relation between two or more variables is in a better way. necessary for many applications. Regression analysis Two purposes are met in this article. The first goal is to is a statistical gadget for discovering these relations. present a new T2FR model with triangular secondary Since people use linguistic terms to judge and evalumembership functions that do not create much computaate things in their lives, FR models are suitable for tional complexity. The second goal is to show the ability of these applications. the proposed model to solve real problems. There are two types of FR models. The first type is the The proposed model is a T2FR model established by afuzzy linear regression (FLR) model introduced by Tanaplane concepts that has shown its ability to forecast the ka [7]–[9]. Instead of finding fuzzy coefficients in regresTAIEX. This model reduces the 3D features of T2FS to sion models, this model solves a linear programming IT2FS. IT2FS can be fully described by a footprint of problem. The second type is based on the least square uncertainty (FOU). The FOU is created with two fuzzy error method [10]. membership functions of type-1, lower membership funcMany real-life phenomena can use FR analysis [11]– tion (LMF), and upper membership function (UMF). [17]. Many studies have been performed on FR models Therefore, the T2FR model is made by developing these because FR has many applications [2], [18]–[21]. Most two type-1 fuzzy functions. previous research has been conducted on type-1 fuzzy One of the primary advantages of this model is that it models. Considering that the T2FS models the ambiguihas more straightforward calculation and imaginable ty and uncertainty of real-world phenomena better, it is concepts in addition to its superior accuracy. This intellimore reasonable to conduct studies on T2F regression gible model helps researchers to use our developed (T2FR). Nevertheless, those variables are difficult to TT2FLR model in other applications like weather and cope given their 3D features. It is essential to present stock value predictions. 52

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A Review of Fuzzy Methods Definition 1: The principal membership function (PrMF) is described as n A = 8x ! X u /x, where fx ^u h = 1. u is normal if: sup n Au ^ x h = 1. Definition 2: A T1FS A u will be Definition 3: If its UMF is normal, an IT2FS A normal (sup nr Au ^ x h = 1). u can be proDefinition 4: A perfectly normal IT2FS A vided when both its UMF and LMF are normal. Definition 5: T2FS can be normal provided that it has a PrMF and FOU is a normal IT2FS. Definition 6: T2FS is perfectly normal where it has a normal PrMF and its FOU is a perfectly normal IT2FS. u , denoted by A u au, Definition 7: An a- plane of a T2FS, A t , in which is the union of the primary memberships of A secondary grades are equal to or higher than au (Figure 1) r a h, where n A ^ x h = 6n A ^ x h, n Ar ^ x h@ [18]. Here, A Va ^ A a, A (Figure 2) [31]. Definition 8: The a- cut of an IT2FS, A V , is a nonfuzzy r a h . Here, n A ^ x h = set described as follows: A Va ^ A a, A r ^ h ^ h 6n A x , n A x @ (Figures 2 and 3) [31]. Regarding Bajestani et  al. [22], this kind of IT2FS can u i = 66a , ar i@, b i, 6c , cr i@@ such that LMFAu = 6ar i, b i, c @, deduced as A i i i u UMFA = 6a i, b i, cr i@ and a i # ar i # b i # c i # cr i . Figure 3 shows a perfectly normal triangular T2FS that is employed in this article. The higher the value of the second-order membership function, the thicker the shadow. V ?

Thus, the supports of UMF and LMF are obtained as the following [31]: u j X ^ji h Yu i = 66 Ay i, Ay i@, By i, 6Cy i, Cy i@@ = |A q

j =1

= | 66a j, ar j@, b j, 6c j, cr j@@ X ^ji h q



=