Concepts, Applications, Experimentation and Analysis of Wireless Sensor Networks: Concepts, Applications, Experimentation and Analysis [2 ed.] 9783030580148, 9783030580155

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Concepts, Applications, Experimentation and Analysis of Wireless Sensor Networks: Concepts, Applications, Experimentation and Analysis [2 ed.]
 9783030580148, 9783030580155

Table of contents :
Preface
Contents
About the Author
List of Acronyms
List of Figures
List of Tables
Part I: WSNs Concepts and Applications
Chapter 1: Introduction
1.1 Sensing, Senses, and Sensors
1.2 Preliminaries of Wireless Sensor Networks
1.3 Mobile Ad Hoc Networks (MANETs)
1.4 Wireless Mesh Networks (WMNs)
1.5 Closer Perspective to WSNs
1.5.1 Wireless Sensor Nodes
1.5.2 Architecture of WSNs
1.6 Types of WSNs
1.6.1 Terrestrial WSNs
1.6.2 Underground WSNs
1.6.3 Underwater Acoustic Sensor Networks (UASNs)
1.6.4 Multimedia WSNs
1.6.5 Mobile WSNs
1.7 Performance Metrics of WSNs
1.8 WSN Standards
1.8.1 IEEE 802.15.4 Low-Rate WPANs
1.8.2 ZigBee
1.8.3 WirelessHART
1.8.4 ISA100.11a
1.8.5 6LoWPAN
1.8.6 IEEE 802.15.3
1.8.7 Wibree, BLE
1.8.8 Z-Wave
1.8.9 Impulse Radio Ultra-Wide Bandwidth Technology, 802.15.4a
1.8.10 INSTEON
1.8.11 Wavenis
1.8.12 ANT
1.8.13 MyriaNed
1.8.14 EnOcean
1.9 Conclusion for a Beginning
1.10 Exercises
References
Chapter 2: Protocol Stack of WSNs
2.1 Introduction
2.2 Physical Layer
2.3 Data Link Layer
2.4 Network Layer
2.5 Transport Layer
2.6 Application Layer
2.7 Cross-Layer Protocols for WSNs
2.8 Conclusion for Continuation
2.9 Exercises
References
Chapter 3: WSN Applications
3.1 Applications Categories, Challenges, and Design Objectives
3.1.1 Functional Challenges of Forming WSNs
3.1.2 Design Objectives of WSNs
3.2 Military Applications
3.2.1 Countersniper System for Urban Warfare
3.2.1.1 Architecture
Hardware Platform
Software Structure
3.2.1.2 Detection
3.2.1.3 Routing Integrated Time Synchronization
3.2.1.4 Sensor Fusion
Range Estimation
3.2.1.5 Experimentation
3.2.2 Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors
3.2.2.1 Hardware
3.2.2.2 Software Architecture
3.2.2.3 Detection Algorithm
3.2.2.4 Sensor Fusion
3.2.2.5 Results
3.2.3 Shooter Localization Using Soldier-Worn Gunfire Detection Systems
3.2.3.1 Mathematical Formulation
3.2.3.2 Data Fusion at Sensor Node Level
3.2.3.3 Data Fusion at the Central Node
3.2.3.4 Results
3.3 Industrial Applications
3.3.1 On the Application of WSNs in Condition Monitoring and Energy Usage Evaluation for Electric Machines
3.3.1.1 Energy Evaluation and Condition Monitoring
Energy Usage Evaluation
Condition Monitoring
Additional Requirements
3.3.1.2 Energy Evaluation and Condition Monitoring using WSNs
System Description
Energy Usage Evaluation
Motor Condition Monitoring
Applicability Analysis
3.3.1.3 Experimentation Results
Energy Usage Evaluation: Motor Efficiency Estimation
Condition Monitoring – Detection of Air-Gap Eccentricities
3.3.2 Breath: An Adaptive Protocol for Industrial Control Applications Using WSNs
3.3.2.1 System Setup
3.3.2.2 The Breath Protocol
3.3.2.3 The Breath Protocol Stack
3.3.2.4 State Machine Description
3.3.2.5 Results and Experimentation
3.3.3 Requirements, Drivers, and Analysis of WSN Solutions for the Oil and Gas Industry
3.3.3.1 Technical Requirements
Long Battery Lifetime
Quantifiable Network Performance
Friendly Coexistence with WLAN
Security
Open Standardized Systems
3.3.3.2 Proprietary Solutions Based on IEEE 802.15.4
3.3.3.3 SmartMesh Experimentation and Interpretations
Network Performance
Coexistence with IEEE 802.11b
Power Consumption
Security
Open Standardized Systems
3.4 Environmental Applications
3.4.1 Assorted Applications
3.4.1.1 Large-Scale Habitat Monitoring
3.4.1.2 Environmental Monitoring
3.4.1.3 Precision Agriculture
3.4.1.4 Macroscope in the Redwoods
3.4.1.5 Active Volcano Monitoring
3.4.1.6 Sensor and Actuator Networks on the Farm
3.4.1.7 Cultural Property Protection
3.4.1.8 Underground Structure Monitoring
3.4.1.9 Foxhouse Project
3.4.1.10 SensorScope for Environmental Monitoring
3.4.1.11 A Biobotic Distributed Sensor Network for Under-Rubble Search and Rescue
Mobile Sensor Nodes and Biobotic Agents
Biobotic Control Demonstrations
Backpack Technologies for Biobots
Sensors for Distributed Sensing and Localization
Localization Technologies and Algorithms
Mapping and Exploration Strategies
3.4.2 A2S: Automated Agriculture System Based on WSN
3.4.2.1 System Architecture
3.4.2.2 Experimentation Results
3.4.3 Living IoT: A Flying Wireless Platform on Live Insects
3.4.3.1 Why Live Insects?
3.4.3.2 Self-Localization of Insects
3.4.3.3 Living IoT Project Design
3.4.3.4 Realized Outcomes
3.4.4 Learning from Researching and Trialing
3.4.4.1 Hardware and Software Development
Consider Local Conditions
Sensor Packaging
Keep It Small and Simple
Think Embedded
Get All Data You Can
Data That Is Useful
3.4.4.2 Testing and Deployment Preparation
Check for Interferences
Data You Can Trust
Be Consistent
3.4.4.3 Deployments
Consider Local Conditions: Once Again
Get a Watchdog
Keep All Data
Data You Can Interpret
Traceability
3.5 Healthcare Applications
3.5.1 Body Area Network Subsystem
3.5.1.1 Power Consumption
3.5.1.2 Output Transmission Power of the Sensor Nodes
3.5.1.3 Unobtrusiveness
3.5.1.4 Mobility and Portability
3.5.1.5 Real-Time Availability and Reliable Communications
3.5.1.6 Multihop Design
3.5.1.7 Security
3.5.2 Personal Area Network Subsystem
3.5.2.1 Contextual Information Acquisition and Location Tracking
3.5.2.2 Modular and Scalable Design
3.5.2.3 Efficient Locating Algorithms
3.5.2.4 Energy Efficiency of the MAC Layer
3.5.2.5 Self-Organization Between Nodes
3.5.3 Gateway to the Wide Area Networks
3.5.3.1 Local Processing Capability at the BAN and PAN Subsystems
3.5.3.2 Security
3.5.4 WANs for Healthcare Applications
3.5.5 End-User Healthcare Monitoring Application
3.5.5.1 Security
3.5.5.2 Privacy
3.5.5.3 Reliability
3.5.5.4 Middleware Design
3.5.5.5 Context Awareness
3.5.5.6 Seamless Healthcare Tracking and Monitoring System
3.5.6 Categorization and Design Features of WSN Healthcare Applications
3.5.6.1 Applications Prototypes
3.5.6.2 Wearable and Implantable Systems
3.5.6.3 Design Features of WSN Healthcare Applications
3.5.7 Using Heterogeneous WSNs in a Telemonitoring System for Healthcare
3.5.7.1 SYLPH Platform
3.5.7.2 SYLPH Services
3.5.7.3 SYLPH Directory Nodes
3.5.7.4 Telemonitoring System Implementation
3.5.7.5 Experimentation Results
3.6 Daily Life Applications
3.6.1 An Intelligent Car Park Management System Based on WSNs
3.6.1.1 Car Parks Requirements
3.6.1.2 System Overview
Hardware Components
Structure of the WSN-Based Application System
Intelligent Car Park Management System
3.6.1.3 System Implementation
Functional Components of the System
Event-Driven Processing
3.6.1.4 System Evaluation
3.6.2 Wireless Sensor Networking of Everyday Objects in a Smart Home Environment
3.6.2.1 Requirements for WSNs in Smart Home Environments
3.6.2.2 System Overview
Wireless Personal Area Network
Personal Server Running an Activity-Centered Computing Middleware
Experimental Setup
System Evaluation
Wireless Communication: Transmission-Reception Range and Signal Strength Measures
Sensing Precision and Recall Values
3.6.3 What Else?
3.7 Multimedia Applications
3.7.1 Network Architecture
3.7.2 Design Issues of WMSNs
3.7.3 WMSN Applications
3.7.4 Hardware Platforms of WMSNs
3.7.4.1 Classification of Wireless Motes
3.7.4.2 Camera Motes Features
3.7.4.3 Available Camera Mote Platforms
Cyclops
Panoptes
Address-Event Imagers
eCAM
WiSN
FireFly Mosaic
MeshEye
MicrelEye
WiCa
CITRIC
ACME Fox Board Camera Platform
Vision Mesh
3.7.4.4 Distributed Smart Cameras
Occlusion
Pixels on Target
Field of View
Tracking
3.8 Robotic WSNs (RWSNs)
3.8.1 Mobility in WSNs
3.8.2 Robotics and WSNs
3.8.2.1 What Is a RWSN?
3.8.2.2 What Kind of Research Works Are RWSN Related?
3.8.2.3 What Are the System Components and Algorithms Required for RWSNs?
3.9 Conclusion for Further
3.10 Exercises
References
Chapter 4: Transport Protocols for WSNs
4.1 Presumptions and Considerations of Transport Protocols in WSNS
4.2 Obsessions of Transport Protocols for WSNs
4.2.1 Transport Protocol Performance Metrics
4.2.1.1 Energy Efficiency
4.2.1.2 Reliability
4.2.1.3 QoS Metrics
4.2.1.4 Fairness
4.2.2 Congestion Control
4.2.3 Loss Recovery
4.2.3.1 Loss Detection and Notification
4.2.3.2 Retransmission-Based Loss Recovery
4.3 Transport Protocols for WSNs
4.3.1 Congestion Detection and Avoidance (CODA)
4.3.2 Event-to-Sink Reliable Transport (ESRT)
4.3.3 Reliable Multi-Segment Transport (RMST)
4.3.4 Pump Slowly Fetch Quickly (PSFQ)
4.3.5 GARUDA
4.3.6 Tiny TCP/IP
4.3.7 Sensor TCP (STCP)
4.3.8 SenTCP
4.3.9 Trickle
4.3.10 Fusion
4.3.11 Asymmetric and Reliable Transport (ART)
4.3.11.1 Reliable Query Transfer
4.3.11.2 Reliable Event Transfer
4.3.11.3 Distributed Congestion Control
4.3.12 Congestion Control and Fairness for Many-to-One Routing in Sensor Networks (CCF)
4.3.13 Priority-Based Congestion Control Protocol (PCCP)
4.3.14 Siphon
4.3.15 Reliable Bursty Convergecast (RBC)
4.3.16 More TCP Protocols for WSNs
4.4 Conclusion for Enrichment
4.5 Exercises
References
Chapter 5: Cross-Layer Protocols for WSNs
5.1 Why Cross-Layering in WSNs
5.2 Cross-Layer Design Approaches
5.2.1 Layers Interactions
5.2.1.1 Cross-Layering MAC and Network Layers
Cross-Layer Network Formation for Energy-Efficient IEEE 802.15.4/ZigBee WSNs (PANEL)
A Cross-Layer Routing Protocol for Balancing Energy Consumption in WSNs (CLB)
5.2.1.2 Cross-Layering Physical and MAC and Network Layers
Cross-Layer Optimized Routing in WSNs with Duty-Cycle and Energy Harvesting (TPGFPlus)
5.2.2 Single-Layer Integrated Module
5.2.2.1 A Cross-Layer Protocol for Efficient Communication in WSNs (XLP)
5.3 Cross-Layer Design for WSNs Security
5.3.1 Challenges of Layered Security Approaches
5.3.2 Limitations of Layered Security Approaches
5.3.3 Guidelines for Securing WSNs
5.3.4 Trends in Cross-Layer Design for Security
5.3.5 Proposals for Cross-Layer Design for Security
5.4 Conclusion for Reality
5.5 Exercises
References
Part II: WSNs Experimentation and Analysis
Chapter 6: Testbeds for WSNs
6.1 WSN Testbeds Principles
6.1.1 Requirements from Testbeds Deployment
6.1.1.1 Additional Requirements
6.1.1.2 User Requirements from a Testbed
6.1.1.3 Operator Requirements from a Testbed
6.1.2 Full-Scale and Miniaturized Testbeds
6.1.3 Virtualizing and Federating Testbeds
6.1.3.1 Virtual Links and Federated Testbeds
6.1.3.2 Topology Virtualization
6.2 Testbeds Illustrated
6.2.1 ORBIT
6.2.1.1 Hardware
ORBIT Grid
Outdoor Testbed
Sandboxes
Chassis Manager
6.2.1.2 Software
Experiment Control
Measurement and Result Collection
6.2.2 MoteLab
6.2.2.1 Technical Details
MoteLab Hardware
MySQL Database Back-End
Web Interface
DBLogger
Job Daemon
User Quotas, Direct Node Access, and Power Measurement
6.2.2.2 Use Models
Batch Use
Real-Time Access
6.2.2.3 MoteLab Applications
6.2.3 Meerkats
6.2.3.1 Hardware
6.2.3.2 Software
Resource Manager
Visual Processing
Communication
6.2.3.3 Energy Consumption Characterization Benchmark
6.2.3.4 Image Acquisition Analysis
6.2.4 MiNT
6.2.4.1 MiNT Architecture
Core Nodes
Controller Node
6.2.4.2 Experimentation on MiNT
Experiment Control
Experiment Analysis
Fidelity of MiNT
MiNT Limitations
6.2.4.3 Hybrid Simulation
Implementation Issues
Hybrid Simulation vs. Pure Simulation
Signal Propagation
Error Characteristics
6.2.5 MiNT-m
6.2.5.1 MiNT-m Architecture
Hardware Components
Software Components
6.2.5.2 Using MiNT-m
Experiment Configuration
Experiment Execution
Experiment Analysis
6.2.5.3 Autonomous Node Mobility
Position and Orientation Tracking
Node Trajectory Determination
24×7 Autonomous Operations and Auto-recharging
6.2.5.4 Hybrid Simulation
Pause/Breakpointing
Rollback Execution
Performance
6.2.6 Kansei
6.2.6.1 Kansei Composition
Hardware Infrastructure
The Stationary Array
Portable Array
Mobile Array
Director: A Uniform Remotely Accessible Framework for Multi-tier WSN Applications
Director Architecture
6.2.6.2 High Fidelity Sensor Data Generation Tools
Sample-Based Modeling Tools
Synthetic Data Generation Using Parametric Models
Probabilistic Modeling Tools
6.2.6.3 Hybrid Simulation
6.2.7 Trio
6.2.7.1 Trio Architecture
Tier-1: The Trio Node
Sustainable Operation
Efficient Physical Interaction
Fail-Safe Flexibility
Tier-2: A Network of Gateways
Tier-3: The Root Server
Network Health Monitoring
Power Monitoring
Monitoring Network Programming
Monitoring and Control of Applications
Tier-4: Client Applications
6.2.7.2 Experimenting with Trio
Familiarities with Renewable Energy
Limited Availability
Emergency Battery Daemon
Epidemic Protocol Failures
Variability at Scale
6.2.8 TWIST
6.2.8.1 TWIST Architecture
Sensor Nodes
Testbed Sockets and USB Cabling
USB Hubs
Super Nodes
Server
Control Station
6.2.8.2 TWIST Installation
Matching SUE and TWIST Architectures
Programming and Time Synchronization
Power Supply Control
Management
6.2.8.3 TWIST Deployment
6.2.9 SignetLab
6.2.9.1 Hardware
Deployment Space
Sensor Nodes
Backplane Connection
6.2.9.2 Software Tool
6.2.9.3 Analysis of SignetLab
6.2.10 WISEBED
6.2.10.1 Architecture
6.2.10.2 WISEBED Compatible Testbeds
6.2.11 Indriya
6.2.11.1 Indriya Composition
Motes
Sensors
USB Active Cables
Design of a Back-Channel for Remote Programming
User Interface
6.2.11.2 Indriya Compared
6.2.12 GENI
6.2.12.1 Federated WSN Fabrics
Clearinghouse Tasks
Federation Services
Authorization Services
Accountability Services
Resource Representation
Resource Discovery
Resource Allocation
Site Requirements
Sliceability
Virtualization
Programmability
Researcher Requirements
Resource Utilization
Resource Translation
6.2.12.2 Why to Use GENI?
6.2.12.3 Key GENI Concepts
Project
Slice
Aggregates
The GENI AM API and GENI RSpecs
Getting Access to GENI and GENI Resources
Tying up All Together: The GENI Experimenter Workflow
Experiment Setup
Experiment Execution
Finishing up
6.2.13 Further Testbeds
6.2.13.1 Emulab
6.2.13.2 PlanetLab
6.2.13.3 Mobile Emulab
6.2.13.4 SenseNet
6.2.13.5 Ubiquitous Robotics
6.3 Conclusion for Extension
6.4 Exercises
References
Chapter 7: Simulators and Emulators for WSNs
7.1 WSN Testbeds, Simulators, and Emulators
7.2 Modeling and Simulation
7.2.1 Basic Definitions
7.2.2 Validation and Verification
7.3 Simulation Principles and Practice
7.3.1 Simulating the Advance of Time
7.3.1.1 The Time-Slicing Approach
7.3.1.2 The Discrete-Event Simulation Approach
7.3.1.3 The Three-Phase Simulation Approach
7.3.1.4 The Continuous Simulation Approach
7.3.2 Proof of Concept
7.3.3 Common Simulation Shortcomings
7.3.3.1 Simulation Setup
Simulation Type
Model Validation and Verification
PRNG Validation and Verification
Variable Definition
Scenario Development
7.3.3.2 Simulation Execution
Setting the PRNG Seed
Scenario Initialization
Metric Collection
7.3.3.3 Output Analysis
Single Set of Data
Statistical Analysis
Confidence Intervals
7.3.3.4 Publishing
7.3.4 Unreliable Simulation Revealed
7.3.5 The Price of Simulation
7.4 Simulators and Emulators
7.4.1 The Network Simulator (ns-2)
7.4.2 The Network Simulator (ns-3)
7.4.3 GloMoSim
7.4.3.1 Parsec
7.4.3.2 Visualization Tool
7.4.3.3 GloMoSim Library
7.4.3.4 Aggregation
Node Aggregation
Layer Aggregation
7.4.4 OPNET
7.4.4.1 Hierarchical Modeling
Network Model
Node Model
Process Model
7.4.4.2 Data Generation
Probe Editor
Analysis Tool
Filter Tool
7.4.5 OMNeT++
7.4.5.1 The Design of OMNeT++
Model Structure
The NED Language
Graphical Editor
Separation of Model and Experiments
Simple Module Programming Model
Design of the Simulation Library
Parallel Simulation Support
Real-Time Simulation and Network Emulation
Animation, Tracing, and Visualizing Dynamic Behavior
7.4.6 TOSSIM
7.4.7 ATEMU
7.4.8 Avrora
7.4.9 EmStar
7.4.9.1 Experimentation
Pure Simulation
Testbeds
Emulation
EmTOS
7.4.10 SensorSim
7.4.11 NRL SensorSim
7.4.12 J-Sim
7.4.12.1 ACA Overview
Component
Component Hierarchy
Port
Contract
7.4.12.2 J-Sim Framework
Communication Model
Power Model
7.4.12.3 Network Emulation
7.4.12.4 J-Sim Performance Compared
Target Tracking
Using GPSR Routing Protocol
7.4.13 Prowler/JProwler
7.4.13.1 Prowler Framework
Radio Propagation Models
Signal Reception and Collisions
MAC Layer Model
The Application Layer
7.4.13.2 Optimization Framework
7.4.13.3 Prowler Performance
7.4.13.4 JProwler
7.4.14 SENS
7.4.14.1 Simulator Structure
Application Components
Network Components
Physical Components
Environment Component
7.4.14.2 Simulation Examples
Spanning Tree
Simplified Localization
7.4.14.3 SENS Performance
7.4.15 Sense
7.4.15.1 Component-Based Design
7.4.15.2 Sensor Network Simulation Components
7.4.15.3 Components Repository
7.4.15.4 Performance Comparison
7.4.16 Shawn
7.4.16.1 Architecture
Models
Sequencer
Simulation Environment
7.4.16.2 Shawn Compared
7.4.17 SenSim
7.4.17.1 SenSim Design
Coordinator Module
Hardware Model
Wireless Channel Model
Sensor Node Stack
7.4.18 PAWiS
7.4.18.1 Structure and Functions
Modularization
CPU
Timing
Environment and Air
Power Simulation
Dynamic Behavior
7.4.18.2 Optimization
7.4.19 MSPsim
7.4.20 Castalia
7.4.21 MiXiM
7.4.21.1 MiXiM Base Models
Environmental Model
Connection Modeling
Nodes Connectivity
Wireless Channel Models
Physical Layer Models
7.4.22 NesCT
7.4.23 Sunshine
7.4.23.1 SUNSHINE Components
7.4.23.2 SUNSHINE Functioning
7.4.23.3 Cross-Domain Interface
7.4.23.4 SUNSHINE Compared
7.4.24 NetTopo
7.5 Conclusion for Takeoff
7.6 Exercises
References
Part III: WSNs Manufacturers and Datasheets
Chapter 8: WSNs Manufacturers
8.1 Adaptive Wireless Solutions (Adaptive Wireless Solutions 2015)
8.2 AlertMe (AlertMe 2014) and British Gas (British Gas 2015)
8.3 ANT Wireless Division of Dynastream (Dynastream Innovations 2014)
8.4 Atmel (Atmel 2015)
8.5 Cisco (Cisco 2015)
8.6 Coalesenses (Coalesenses 2014)
8.7 Crossbow Technologies (Aol 2015)
8.8 Dust Networks (Dust Networks 2015)
8.9 EasySen (EasySen 2015)
8.10 EcoLogicSense (EcoLogicSense 2015)
8.11 EpiSensor (EpiSensor 2015)
8.12 ERS (ERS 2015)
8.13 GainSpan (GainSpan 2015)
8.14 Infineon (Infineon 2015)
8.15 Libelium (Libelium 2015)
8.16 MEMSIC (MEMSIC 2015)
8.17 Millennial Net (Millennial Net 2012)
8.18 Moog Crossbow (Moog Crossbow 2014)
8.19 Moteiv (Sensors Online 2007)
8.20 National Instruments (National Instruments 2015)
8.21 OmniVision Technologies (OmniVision Technologies 2011)
8.22 Sensirion (Sensirion 2015)
8.23 Shimmer (Shimmer 2015)
8.24 Silicon Labs (Sillicon Labs 2015)
8.25 SOWNet Technologies (SOWNet Technologies 2014)
8.26 SPI (SPI 2015)
8.27 Terabee (Terabee 2015)
8.28 Texas Instruments (TI 2015)
8.29 Valarm (Valarm 2015)
8.30 WhizNets (WhizNets 2015)
8.31 Willow Technologies (Willow Technologies 2012)
8.32 Xandem (Xandem 2015)
References
Chapter 9: Datasheets
9.1 Agilent ADCM-1670 CIF Resolution CMOS Camera Module (Agilent Technologies 2003a)
9.2 Agilent ADCM-1700-0000 CMOS Camera Module (Agilent Technologies 2003b)
9.3 Agilent ADCM-2650 CMOS Camera Module (Agilent Technologies 2003c)
9.4 Agilent ADNS-3060 Optical Mouse Sensor (Agilent Technologies 2004)
9.5 AL440B High Speed FIFO Field Memory (AverLogic Technologies 2002)
9.6 Atmel AT29BV040A Flash Memory (Atmel 2003)
9.7 Atmel AT91 ARM Thumb-Based Microcontrollers (Atmel 2008)
9.8 Atmel AT91SAM ARM-Based Embedded MPU (Atmel 2011c)
9.9 Atmel Microcontroller with 4/8/16 K Bytes In-System Programmable Flash (Atmel 2011b)
9.10 Atmel Microcontroller with 128 K Bytes In-System Programmable Flash (Atmel 2011a)
9.11 Atmel FPSLIC (Atmel 2002)
9.12 Bluegiga WT12 (Bluegiga Technologies 2007)
9.13 C8051F121 Mixed-Signal MCU (Silicon Laboratories 2004)
9.14 CC1000 (Texas Instruments 2007a)
9.15 CC1020 (Texas Instruments 2014a)
9.16 CC1100 (Texas Instruments 2005a)
9.17 CC1101 (Texas Instruments 2014b)
9.18 CC2420 (Texas Instruments 2005b)
9.19 CC2430 (Texas Instruments 2006)
9.20 CC2431 (Texas Instruments 2005c)
9.21 CC2530 (Texas Instruments 2011a)
9.22 CP2102/9 Single-Chip USB to UART Bridge (Silicon Laboratories 2013)
9.23 Digital Compass Solutions HMR3300 (Honeywell 2012)
9.24 DS18B20 Programmable Resolution 1-Wire Digital Thermometer (Maxim Integrated 2008)
9.25 DS18S20 High-Precision 1-Wire Digital Thermometer (Maxim Integrated 2010)
9.26 G-Node G301 (SOWNet Technologies 2014)
9.27 GS-1 Low Frequency Seismometer (Geospace Technologies 2014b)
9.27.1 GS-1 Low Frequency Seismometer
9.28 GS-11D Geophone (Geospace Technologies 2014a)
9.29 Imote2 (Crossbow 2005)
9.30 Intel PXA270 Processor (Intel 2005a)
9.31 Intel StrataFlash Embedded Memory (Intel 2005b)
9.32 Intel StrongARM* SA-1110 (Intel 2000)
9.33 iSense Security Sensor Module (Coalesenses 2014)
9.34 MICA2 Mote (Crossbow 2002a)
9.35 MICA2DOT (Crossbow 2002b)
9.36 MICAz Mote (Crossbow 2006a)
9.37 ML675K Series (Oki Semiconductor 2004)
9.38 MOTE-VIEW 1.2 (Crossbow 2006b)
9.39 MSB-A2 Platform (Baar et al. 2008)
9.40 MSP430F1611 Microcontroller (Texas Instruments 2011b)
9.41 MSP430F2416 Microcontroller (Texas Instruments 2007b)
9.42 MSX-01F Solar Panel (BP Solar 2014)
9.42.1 BP SOLAR – MSX-01F – SOLAR PANEL, 1.2 W
9.43 MTS/MDA (Crossbow 2007a)
9.44 Omron Subminiature Basis Switch (Omron 2014)
9.45 OV528 Serial Bus Camera System (OmniVision Technologies 2002)
9.46 OV6620/OV6120 Single-Chip CMOS Digital Camera (OmniVision Technologies 1999)
9.47 OV7640/OV7140 CMOS VGA CAMERACHIPS (OmniVision Technologies 2003)
9.48 OV9655/OV9155 (OmniVision Technologies 2006)
9.49 PCF50606/605 Single-Chip Power Management Unit+ (Philips 2002)
9.50 PIC18 Microcontroller Family (Microchip 2000)
9.51 Qimonda HYB18L512160BF-7.5 (Qimonda AG 2006)
9.52 SBT30EDU Sensor and Prototyping Board (EasySen LLC 2008a)
9.53 SBT80 Multi-Modality Sensor Board for TelosB Wireless Motes (EasySen LLC 2008a)
9.54 Spartan-3 FPGA (XILINX 2013)
9.55 Stargate (Crossbow 2004)
9.56 Stargate NetBridge (Crossbow 2007b)
9.57 T-Node (SOWNet 2014)
9.58 TC55VCM208ASTN40,55 CMOS Static RAM (Toshiba 2002)
9.59 Telos (Moteiv 2004)
9.60 TinyNode (Dubois-Ferrière et al. 2006, Fig. 9.2)
9.61 Tmote Connect (Moteiv 2006a)
9.62 Tmote Sky (Moteiv 2006b)
9.63 TSL250R, TSL251R, TSL252R Light to Voltage Optical Sensors (TAOS 2001)
9.64 WiEye Sensor Board for Wireless Surveillance and Security Applications (EasySen LLC 2008b)
9.65 WM8950 (Wolfson Microelectronics 2011)
9.66 Xbee/Xbee-PRO OEM RF Modules (MaxStream 2007)
9.67 XC2C256 CoolRunner-II CPLD (XILINX 2007)
9.68 XE1205I Integrated UHF Transceiver (Semtech 2008)
References
Part IV: Ignition
Chapter 10: Takeoff
Index

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