Concepts, Applications, Experimentation and Analysis of Wireless Sensor Networks

Preface
About the Book
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, 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 WSNs 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 80215.3
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: WSNs 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
3.2.1.1.1 Hardware Platform
3.2.1.1.2 Software Structure
3.2.1.2 Detection
3.2.1.3 Routing Integrated Time Synchronization
3.2.1.4 Sensor Fusion
3.2.1.4.1 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
3.3.1.1.1 Energy Usage Evaluation
3.3.1.1.2 Condition Monitoring
3.3.1.1.3 Additional Requirements
3.3.1.2 Energy Evaluation and Condition Monitoring Using WSNs
3.3.1.2.1 System Description
3.3.1.2.2 Energy Usage Evaluation
3.3.1.2.3 Motor Condition Monitoring
3.3.1.2.4 Applicability Analysis
3.3.1.3 Experimentation Results
3.3.1.3.1 Energy Usage Evaluation-Motor Efficiency Estimation
3.3.1.3.2 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
3.3.3.1.1 Long Battery Lifetime
3.3.3.1.2 Quantifiable Network Performance
3.3.3.1.3 Friendly Coexistence with WLAN
3.3.3.1.4 Security
3.3.3.1.5 Open Standardized Systems
3.3.3.2 Proprietary Solutions Based on IEEE 802.15.4
3.3.3.3 SmartMesh Experimentation and Interpretations
3.3.3.3.1 Network Performance
3.3.3.3.2 Coexistence with IEEE 802.11b
3.3.3.3.3 Power Consumption
3.3.3.3.4 Security
3.3.3.3.5 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
3.4.1.11.1 Mobile Sensor Nodes and Biobotic Agents
3.4.1.11.2 Biobotic Control Demonstrations
3.4.1.11.3 Backpack Technologies for Biobots
3.4.1.11.4 Sensors for Distributed Sensing and Localization
3.4.1.11.5 Localization Technologies and Algorithms
3.4.1.11.6 Mapping and Exploration Strategies
3.4.1.12 Efficient Data Collection and Tracking with Flying Drones
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
3.4.4.1.1 Consider Local Conditions
3.4.4.1.2 Sensor Packaging
3.4.4.1.3 Keep It Small and Simple
3.4.4.1.4 Think Embedded
3.4.4.1.5 Get All Data You Can
3.4.4.1.6 Data that Is Useful
3.4.4.2 Testing and Deployment Preparation
3.4.4.2.1 Check for Interferences
3.4.4.2.2 Data You Can Trust
3.4.4.2.3 Be Consistent
3.4.4.3 Deployments
3.4.4.3.1 Consider Local Conditions-Once Again
3.4.4.3.2 Get a Watchdog
3.4.4.3.3 Keep all Data
3.4.4.3.4 Data You Can Interpret
3.4.4.3.5 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
3.6.1.2.1 Hardware Components
3.6.1.2.2 Structure of the WSN-s based Application System
3.6.1.2.3 Intelligent Car Park Management System
3.6.1.3 System Implementation
3.6.1.3.1 Functional Components of the System
3.6.1.3.2 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
3.6.2.2.1 Wireless Personal Area Network
3.6.2.2.2 Personal Server Running an Activity-Centered Computing Middleware
3.6.2.2.3 Experimental Setup
3.6.2.2.4 System Evaluation
3.6.2.2.4.1 Wireless Communication: Transmission Reception Range and Signal Strength measures
3.6.2.2.4.2 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 WMSNs 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
3.7.4.3.1 Cyclops
3.7.4.3.2 Panoptes
3.7.4.3.3 Address-Event Imagers
3.7.4.3.4 eCAM
3.7.4.3.5 WiSN
3.7.4.3.6 FireFly Mosaic
3.7.4.3.7 MeshEye
3.7.4.3.8 MicrelEye
3.7.4.3.9 WiCa
3.7.4.3.10 CITRIC
3.7.4.3.11 ACME Fox Board Camera Platform
3.7.4.3.12 Vision Mesh
3.7.4.4 Distributed Smart Cameras
3.7.4.4.1 Occlusion
3.7.4.4.2 Pixels on Target
3.7.4.4.3 Field of View
3.7.4.4.4 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
Part II: Network and Transport Layers, Cross-Layering
Chapter 4: Energy and Lifetime Aware Routing Protocols for WSNs
4.1 WSNs Energy-Driven Considerations
4.2 WSNs Energy and Lifetime Terminology, Models, and Metrics
4.2.1 WSNs Energy and Lifetime Terminology
4.2.2 Energy Efficiency Metrics
4.3 Traffic Patterns, Data Collection and Aggregation, and Clustering in WSNs
4.3.1 Traffic Patterns in WSNs
4.3.2 Data Collection and Aggregation in WSNs
4.3.3 Clustering in WSNs
4.4 Homogeneous and Heterogeneous WSNs
4.5 Single-Hop and Multihop Transmission
4.6 Design Issues of Energy and Lifetime Aware Routing Protocols for WSNs
4.6.1 Why Routing Protocols for WSNs Are Different?
4.6.2 Factors That Influence the Design of Energy and Lifetime Aware Routing Protocols for WSNs
4.6.3 Goals of Energy and Lifetime Aware Routing Protocols for WSNs
4.7 Energy-Efficient Routing Protocols
4.7.1 Network Structure-Based Approach
4.7.1.1 Flat Networks Routing Protocols
4.7.1.1.1 Proactive or Table-Driven Routing Protocols
4.7.1.1.2 Reactive or Source-Initiated on-Demand Routing Protocols
4.7.1.1.3 Proactive (Table-Driven) Versus Reactive (On-Demand) Routing Protocols
4.7.1.2 Hierarchical Networks Routing Protocols
4.7.2 Communication Model-Based Approach
4.7.2.1 Query-Based Routing Protocols
4.7.2.2 Coherent and Non-Coherent-Based Routing Protocols
4.7.2.3 Negotiation-Based Routing Protocols
4.7.3 Topology-Based Approach
4.7.3.1 Location-Based Routing Protocols
4.7.3.2 Mobile Agent-Based Routing Protocols
4.7.4 Reliable Routing Approach
4.7.4.1 Multipath-Based Routing Protocols
4.7.4.2 QoS-Based Routing Protocols
4.7.5 Notable Outlines
4.8 Energy-Efficient and Energy-Balanced Routing Protocols
4.8.1 Multihop Communication
4.8.1.1 Multihop Clustering Techniques
4.8.1.1.1 Cluster-Head Selection
4.8.1.1.2 Forming the Cluster
4.8.1.1.3 WSN Partitioning into Optimal Clusters
4.8.1.2 Load-Balanced Tree Techniques
4.8.1.2.1 Multipath Approach
4.8.1.2.2 Single-Path Approach
4.8.2 Single-Hop Communication
4.9 Energy-Efficient Routing Protocols for Homogeneous and Heterogeneous WSNs
4.9.1 Routing Protocols for Homogeneous WSNs
4.9.1.1 Static Homogeneous WSNs
4.9.1.2 Mobile Homogeneous WSNs
4.9.2 Routing Protocols for Heterogeneous WSNs
4.9.2.1 Static Heterogeneous WSNs
4.9.2.2 Mobile Heterogeneous WSNs
4.9.3 Recapitulation
4.10 Conclusion for Good Paths
4.11 Exercises
References
Chapter 5: Transport Protocols for WSNs
5.1 Presumptions and Considerations of Transport Protocols in WSNS
5.2 Obsessions of Transport Protocols for WSNs
5.2.1 Transport Protocols Performance Metrics
5.2.1.1 Energy Efficiency
5.2.1.2 Reliability
5.2.1.3 QoS Metrics
5.2.1.4 Fairness
5.2.2 Congestion Control
5.2.3 Loss Recovery
5.2.3.1 Loss Detection and Notification
5.2.3.2 Retransmission-Based Loss Recovery
5.3 Transport Protocols for WSNs
5.3.1 COngestion Detection and Avoidance (CODA)
5.3.2 Event-to-Sink Reliable Transport (ESRT)
5.3.3 Reliable Multi-Segment Transport (RMST)
5.3.4 Pump Slowly Fetch Quickly (PSFQ)
5.3.5 GARUDA
5.3.6 Tiny TCP/IP
5.3.7 Sensor TCP (STCP)
5.3.8 SenTCP
5.3.9 Trickle
5.3.10 Fusion
5.3.11 Asymmetric and Reliable Transport (ART)
5.3.11.1 Reliable Query Transfer
5.3.11.2 Reliable Event Transfer
5.3.11.3 Distributed Congestion Control
5.3.12 Congestion Control and Fairness for Many-to-One Routing in Sensor Networks (CCF)
5.3.13 Priority-Based Congestion Control Protocol (PCCP)
5.3.14 Siphon
5.3.15 Reliable Bursty Convergecast (RBC)
5.3.16 More TCP Protocols for WSNs
5.4 Conclusion for Enrichment
5.5 Exercises
References
Chapter 6: Cross-Layer Protocols for WSNs
6.1 Why Cross-Layering in WSNs
6.2 Cross-Layer Design Approaches
6.2.1 Layers Interactions
6.2.1.1 Cross-Layering MAC and Network Layers
6.2.1.1.1 Cross-Layer Network Formation for Energy-Efficient IEEE 802.15.4/ZigBee WSNs (PANEL)
6.2.1.1.2 A Cross-Layer Routing Protocol for Balancing Energy Consumption in WSNs (CLB)
6.2.1.2 Cross-Layering Physical and MAC and Network Layers
6.2.1.2.1 Cross-Layer Optimized Routing in WSNs with Duty Cycle and Energy Harvesting (TPGFPlus)
6.2.2 Single-Layer Integrated Module
6.2.2.1 A Cross-Layer Protocol for Efficient Communication in WSNs (XLP)
6.3 Cross-Layer Design for WSNs Security
6.3.1 Challenges of Layered Security Approaches
6.3.2 Limitations of Layered Security Approaches
6.3.3 Guidelines for Securing WSNs
6.3.4 Trends in Cross-Layer Design for Security
6.3.5 Proposals for Cross-Layer Design for Security
6.4 Conclusion for Reality
6.5 Exercises
References
Part III: WSNs Experimentation and Analysis
Chapter 7: Testbeds for WSNs
7.1 WSN Testbeds Principles
7.1.1 Requirements from Testbed Deployment
7.1.1.1 Additional Requirements
7.1.1.2 User Requirements from a Testbed
7.1.1.3 Operator Requirements from a Testbed
7.1.2 Full-Scale and Miniaturized Testbeds
7.1.3 Virtualizing and Federating Testbeds
7.1.3.1 Virtual Links and Federated Testbeds
7.1.3.2 Topology Virtualization
7.2 Testbeds Illustrated
7.2.1 ORBIT
7.2.1.1 Hardware
7.2.1.1.1 ORBIT Grid
7.2.1.1.2 Outdoor Testbed
7.2.1.1.3 Sandboxes
7.2.1.1.4 Chassis Manager
7.2.1.2 Software
7.2.1.2.1 Experiment Control
7.2.1.2.2 Measurement and Result Collection
7.2.2 MoteLab
7.2.2.1 Technical Details
7.2.2.1.1 MoteLab Hardware
7.2.2.1.2 MySQL Database Back End
7.2.2.1.3 Web Interface
7.2.2.1.4 DBLogger
7.2.2.1.5 Job Daemon
7.2.2.1.6 User Quotas, Direct Node Access, Power Measurement
7.2.2.2 Use Models
7.2.2.2.1 Batch Use
7.2.2.2.2 Real-Time Access
7.2.2.3 MoteLab Applications
7.2.3 Meerkats
7.2.3.1 Hardware
7.2.3.2 Software
7.2.3.2.1 Resource Manager
7.2.3.2.2 Visual Processing
7.2.3.2.3 Communication
7.2.3.3 Energy Consumption Characterization Benchmark
7.2.3.4 Image Acquisition Analysis
7.2.4 MiNT
7.2.4.1 MiNT Architecture
7.2.4.1.1 Core Nodes
7.2.4.1.2 Controller Node
7.2.4.2 Experimentation on MiNT
7.2.4.2.1 Experiment Control
7.2.4.2.2 Experiment Analysis
7.2.4.2.3 Fidelity of MiNT
7.2.4.2.4 MiNT Limitations
7.2.4.3 Hybrid Simulation
7.2.4.3.1 Implementation Issues
7.2.4.3.2 Hybrid Simulation vs. Pure Simulation
7.2.4.3.2.1 Signal Propagation
7.2.4.3.2.2 Error Characteristics
7.2.5 MiNT-m
7.2.5.1 MiNT-m Architecture
7.2.5.1.1 Hardware Components
7.2.5.1.2 Software Components
7.2.5.2 Using MiNT-m
7.2.5.2.1 Experiment Configuration
7.2.5.2.2 Experiment Execution
7.2.5.2.3 Experiment Analysis
7.2.5.3 Autonomous Node Mobility
7.2.5.3.1 Position and Orientation Tracking
7.2.5.3.2 Node Trajectory Determination
7.2.5.3.3 24 x 7 Autonomous Operations and Auto-recharging
7.2.5.4 Hybrid Simulation
7.2.5.4.1 Pause/Breakpointing
7.2.5.4.2 Rollback Execution
7.2.5.4.3 Performance
7.2.6 Kansei
7.2.6.1 Kansei Composition
7.2.6.1.1 Hardware Infrastructure
7.2.6.1.1.1 The Stationary Array
7.2.6.1.1.2 Portable Array
7.2.6.1.1.3 Mobile Array
7.2.6.1.2 Director: A Uniform Remotely Accessible Framework for Multitier WSN Applications
7.2.6.1.2.1 Director Architecture
7.2.6.2 High-Fidelity Sensor Data Generation Tools
7.2.6.2.1 Sample-Based Modeling Tools
7.2.6.2.2 Synthetic Data Generation Using Parametric Models
7.2.6.2.3 Probabilistic Modeling Tools
7.2.6.3 Hybrid Simulation
7.2.7 Trio
7.2.7.1 Trio Architecture
7.2.7.1.1 Tier 1: The Trio Node
7.2.7.1.1.1 Sustainable Operation
7.2.7.1.1.2 Efficient Physical Interaction
7.2.7.1.1.3 Fail-Safe Flexibility
7.2.7.1.2 Tier 2: A Network of Gateways
7.2.7.1.3 Tier 3: The Root Server
7.2.7.1.3.1 Network Health Monitoring
7.2.7.1.3.2 Power Monitoring
7.2.7.1.3.3 Monitoring Network Programming
7.2.7.1.3.4 Monitoring and Control of Applications
7.2.7.1.4 Tier 4: Client Applications
7.2.7.2 Experimenting with Trio
7.2.7.2.1 Familiarities with Renewable Energy
7.2.7.2.2 Limited Availability
7.2.7.2.3 Emergency Battery Daemon
7.2.7.2.4 Epidemic Protocol Failures
7.2.7.2.5 Variability at Scale
7.2.8 TWIST
7.2.8.1 TWIST Architecture
7.2.8.1.1 Sensor Nodes
7.2.8.1.2 Testbed Sockets and USB Cabling
7.2.8.1.3 USB Hubs
7.2.8.1.4 Super Nodes
7.2.8.1.5 Server
7.2.8.1.6 Control Station
7.2.8.2 TWIST Installation
7.2.8.2.1 Matching SUE and TWIST Architectures
7.2.8.2.2 Programming and Time Synchronization
7.2.8.2.3 Power Supply Control
7.2.8.2.4 Management
7.2.8.3 TWIST Deployment
7.2.9 SignetLab
7.2.9.1 Hardware
7.2.9.1.1 Deployment Space
7.2.9.1.2 Sensor Nodes
7.2.9.1.3 Backplane Connection
7.2.9.2 Software Tool
7.2.9.3 Analysis of SignetLab
7.2.10 WISEBED
7.2.10.1 Architecture
7.2.10.2 WISEBED-Compatible Testbeds
7.2.11 Indriya
7.2.11.1 Indriya Composition
7.2.11.1.1 Motes
7.2.11.1.2 Sensors
7.2.11.1.3 USB Active Cables
7.2.11.1.4 Design of a Back Channel for Remote Programming
7.2.11.1.5 User Interface
7.2.11.2 Indriya Compared
7.2.12 GENI
7.2.12.1 Federated WSN Fabrics
7.2.12.1.1 Clearinghouse Tasks
7.2.12.1.1.1 Federation Services
7.2.12.1.1.2 Authorization Services
7.2.12.1.1.3 Accountability Services
7.2.12.1.1.4 Resource Representation
7.2.12.1.1.5 Resource Discovery
7.2.12.1.1.6 Resource Allocation
7.2.12.1.2 Site Requirements
7.2.12.1.2.1 Sliceability
7.2.12.1.2.2 Virtualization
7.2.12.1.2.3 Programmability
7.2.12.1.3 Researcher Requirements
7.2.12.1.3.1 Resource Utilization
7.2.12.1.3.2 Resource Translation
7.2.12.2 Why Use GENI?
7.2.12.3 Key GENI Concepts
7.2.12.3.1 Project
7.2.12.3.2 Slice
7.2.12.3.3 Aggregates
7.2.12.3.4 The GENI AM API and GENI RSpecs
7.2.12.3.5 Getting Access to GENI and GENI Resources
7.2.12.3.6 Tying Up All Together: The GENI Experimenter Workflow
7.2.12.3.6.1 Experiment Setup
7.2.12.3.6.2 Experiment Execution
7.2.12.3.6.3 Finishing Up
7.2.13 Further Testbeds
7.2.13.1 Emulab
7.2.13.2 PlanetLab
7.2.13.3 Mobile Emulab
7.2.13.4 SenseNet
7.2.13.5 Ubiquitous Robotics
7.3 Conclusion for Extension
7.4 Exercises
References
Chapter 8: Simulators and Emulators for WSNs
8.1 WSN Testbeds, Simulators, and Emulators
8.2 Modeling and Simulation
8.2.1 Basic Definitions
8.2.2 Validation and Verification
8.3 Simulation Principles and Practice
8.3.1 Simulating the Advance of Time
8.3.1.1 The Time-Slicing Approach
8.3.1.2 The Discrete-Event Simulation Approach
8.3.1.3 The Three-Phase Simulation Approach
8.3.1.4 The Continuous Simulation Approach
8.3.2 Proof of Concept
8.3.3 Common Simulation Shortcomings
8.3.3.1 Simulation Setup
8.3.3.1.1 Simulation Type
8.3.3.1.2 Model Validation and Verification
8.3.3.1.3 PRNG Validation and Verification
8.3.3.1.4 Variable Definition
8.3.3.1.5 Scenario Development
8.3.3.2 Simulation Execution
8.3.3.2.1 Setting the PRNG Seed
8.3.3.2.2 Scenario Initialization
8.3.3.2.3 Metric Collection
8.3.3.3 Output Analysis
8.3.3.3.1 Single Set of Data
8.3.3.3.2 Statistical Analysis
8.3.3.3.3 Confidence Intervals
8.3.3.4 Publishing
8.3.4 Unreliable Simulation Revealed
8.3.5 The Price of Simulation
8.4 Simulators and Emulators
8.4.1 The Network Simulator (ns-2)
8.4.2 The Network Simulator (ns-3)
8.4.3 GloMoSim
8.4.3.1 PARSEC
8.4.3.2 Visualization Tool
8.4.3.3 GloMoSim Library
8.4.3.4 Aggregation
8.4.3.4.1 Node Aggregation
8.4.3.4.2 Layer Aggregation
8.4.4 OPNET
8.4.4.1 Hierarchical Modeling
8.4.4.1.1 Network Model
8.4.4.1.2 Node Model
8.4.4.1.3 Process Model
8.4.4.2 Data Generation
8.4.4.2.1 Probe Editor
8.4.4.2.2 Analysis Tool
8.4.4.2.3 Filter Tool
8.4.5 OMNeT++
8.4.5.1 The Design of OMNeT++
8.4.5.1.1 Model Structure
8.4.5.1.2 The NED Language
8.4.5.1.3 Graphical Editor
8.4.5.1.4 Separation of Model and Experiments
8.4.5.1.5 Simple Module Programming Model
8.4.5.1.6 Design of the Simulation Library
8.4.5.1.7 Parallel Simulation Support
8.4.5.1.8 Real-Time Simulation, Network Emulation
8.4.5.1.9 Animation, Tracing, and Visualizing Dynamic Behavior
8.4.6 TOSSIM
8.4.7 ATEMU
8.4.8 Avrora
8.4.9 EmStar
8.4.9.1 Experimentation
8.4.9.1.1 Pure Simulation
8.4.9.1.2 Testbeds
8.4.9.1.3 Emulation
8.4.9.1.4 EmTOS
8.4.10 SensorSim
8.4.11 NRL SensorSim
8.4.12 J-Sim
8.4.12.1 ACA Overview
8.4.12.1.1 Component
8.4.12.1.1.1 Component Hierarchy
8.4.12.1.2 Port
8.4.12.1.3 Contract
8.4.12.2 J-Sim Framework
8.4.12.2.1 Communication Model
8.4.12.2.2 Power Model
8.4.12.3 Network Emulation
8.4.12.4 J-Sim Performance Compared
8.4.12.4.1 Target Tracking
8.4.12.4.2 Using GPSR Routing Protocol
8.4.13 Prowler/JProwler
8.4.13.1 Prowler Framework
8.4.13.1.1 Radio Propagation Models
8.4.13.1.2 Signal Reception and Collisions
8.4.13.1.3 MAC-Layer Model
8.4.13.1.4 The Application Layer
8.4.13.2 Optimization Framework
8.4.13.3 Prowler Performance
8.4.13.4 JProwler
8.4.14 SENS
8.4.14.1 Simulator Structure
8.4.14.1.1 Application Components
8.4.14.1.2 Network Components
8.4.14.1.3 Physical Components
8.4.14.1.4 Environment Component
8.4.14.2 Simulation Examples
8.4.14.2.1 Spanning Tree
8.4.14.2.2 Simplified Localization
8.4.14.3 SENS Performance
8.4.15 SENSE
8.4.15.1 Component-Based Design
8.4.15.2 Sensor Network Simulation Components
8.4.15.3 Components Repository
8.4.15.4 Performance Comparison
8.4.16 Shawn
8.4.16.1 Architecture
8.4.16.1.1 Models
8.4.16.1.2 Sequencer
8.4.16.1.3 Simulation Environment
8.4.16.2 Shawn Compared
8.4.17 SenSim
8.4.17.1 SenSim Design
8.4.17.1.1 Coordinator Module
8.4.17.1.2 Hardware Model
8.4.17.1.3 Wireless Channel Model
8.4.17.1.4 Sensor Node Stack
8.4.18 PAWiS
8.4.18.1 Structure and Functions
8.4.18.1.1 Modularization
8.4.18.1.2 CPU
8.4.18.1.3 Timing
8.4.18.1.4 Environment and Air
8.4.18.1.5 Power Simulation
8.4.18.1.6 Dynamic Behavior
8.4.18.2 Optimization
8.4.19 MSPsim
8.4.20 Castalia
8.4.21 MiXiM
8.4.21.1 MiXiM Base Models
8.4.21.1.1 Environmental Model
8.4.21.1.2 Connection Modeling
8.4.21.1.2.1 Nodes Connectivity
8.4.21.1.2.2 Wireless Channel Models
8.4.21.1.3 Physical Layer Models
8.4.22 NesCT
8.4.23 SUNSHINE
8.4.23.1 SUNSHINE Components
8.4.23.2 SUNSHINE Functioning
8.4.23.3 Cross-Domain Interface
8.4.23.4 SUNSHINE Compared
8.4.24 NetTopo
8.5 Conclusion for Takeoff
8.6 Exercises
References
Part IV: WSNs Manufacturers and Datasheets
Chapter 9: WSN Manufacturers
9.1 Adaptive Wireless Solutions (Adaptive Wireless Solutions 2015)
9.2 AlertMe (AlertMe 2014) and British Gas (British Gas 2015)
9.3 ANT Wireless Division of Dynastream (Dynastream Innovations 2014)
9.4 Atmel (Atmel 2015)
9.5 Cisco (Cisco 2015)
9.6 Coalesenses (Coalesenses 2014)
9.7 Crossbow Technologies (Aol 2015)
9.8 Dust Networks (Dust Networks 2015)
9.9 EasySen (EasySen 2015)
9.10 EcoLogicSense (EcoLogicSense 2015)
9.11 EpiSensor (EpiSensor 2015)
9.12 ERS (ERS 2015)
9.13 GainSpan (GainSpan 2015)
9.14 Infineon (Infineon 2015)
9.15 Libelium (Libelium 2015)
9.16 MEMSIC (MEMSIC 2015)
9.17 Millennial Net (Millennial Net 2012)
9.18 Moog Crossbow (Moog Crossbow 2014)
9.19 Moteiv (Sensors Online 2007)
9.20 National Instruments (National Instruments 2015)
9.21 OmniVision Technologies (OmniVision Technologies 2011)
9.22 Sensirion (Sensirion 2015)
9.23 Shimmer (Shimmer 2015)
9.24 Silicon Labs (Sillicon Labs 2015)
9.25 SOWNet Technologies (SOWNet Technologies 2014)
9.26 SPI (SPI 2015)
9.27 Terabee (Terabee 2015)
9.28 Texas Instruments (TI 2015)
9.29 Valarm (Valarm 2015)
9.30 WhizNets (WhizNets 2015)
9.31 Willow Technologies (Willow Technologies 2012)
9.32 Xandem (Xandem 2015)
References
Chapter 10: Datasheets
10.1 Agilent ADCM-1670 CIF Resolution CMOS Camera Module (Agilent Technologies 2003a)
10.2 Agilent ADCM-1700-0000 CMOS Camera Module (Agilent Technologies 2003b)
10.3 Agilent ADCM-2650 CMOS Camera Module (Agilent Technologies 2003c)
10.4 Agilent ADNS-3060 Optical Mouse Sensor (Agilent Technologies 2004)
10.5 AL440B High Speed FIFO Field Memory (AverLogic Technologies 2002)
10.6 Atmel AT29BV040A Flash Memory (Atmel 2003a)
10.7 Atmel AT91 ARM Thumb-Based Microcontrollers (Atmel 2008)
10.8 Atmel AT91SAM ARM-Based Embedded MPU (Atmel 2011c)
10.9 Atmel Microcontroller with 4/8/16 K Bytes in-System Programmable Flash (Atmel 2011b)
10.10 Atmel Microcontroller with 128KBytes in-System Programmable Flash (Atmel 2011a)
10.11 Atmel FPSLIC (Atmel 2002)
10.12 Bluegiga WT12 (Bluegiga Technologies 2007)
10.13 C8051F121 Mixed-Signal MCU (Silicon Laboratories 2004)
10.14 CC1000 (Texas Instruments 2007a)
10.15 CC1020 (Texas Instruments 2014a)
10.16 CC1100 (Texas Instruments 2005a)
10.17 CC1101 (Texas Instruments 2014b)
10.18 C2420 (Texas Instruments 2005b)
10.19 CC2430 (Texas Instruments 2006)
10.20 CC2431 (Texas Instruments 2005c)
10.21 CC2530 (Texas Instruments 2011a)
10.22 CP2102/9 Single-Chip USB to UART Bridge (Silicon Laboratories 2013)
10.23 Digital Compass Solutions HMR3300 (Honeywell 2012)
10.24 DS18B20 Programmable Resolution 1-Wire Digital Thermometer (Maxim Integrated 2008)
10.25 DS18S20 High-Precision 1-Wire Digital Thermometer (Maxim Integrated 2010)
10.26 G-Node G301 (SOWNet Technologies 2014)
10.27 GS-1 Low Frequency Seismometer (Geospace Technologies 2014b)
10.28 GS-11D Geophone (Geospace Technologies 2014a)
10.29 Imote2 (Crossbow 2005)
10.30 Intel PXA270 Processor (Intel 2005a)
10.31 Intel StrataFlash Embedded Memory (Intel 2005b)
10.32 Intel StrongARM* SA-1110 (Intel 2000)
10.33 iSense Security Sensor Module (Coalesenses 2014)
10.34 MICA2 Mote (Crossbow 2002a)
10.35 MICA2DOT (Crossbow 2002b)
10.36 MICAz Mote (Crossbow 2006a)
10.37 ML675K Series (Oki Semiconductor 2004)
10.38 MOTE-VIEW 1.2 (Crossbow 2006b)
10.39 MSB-A2 Platform (Baar et al. 2008)
10.40 MSP430F1611 Microcontroller (Texas Instruments 2011b)
10.41 MSP430F2416 Microcontroller (Texas Instruments 2007b)
10.42 MSX-01F Solar Panel (BP Solar 2014)
10.43 MTS/MDA (Crossbow 2007a)
10.44 Omron Subminiature Basis Switch (Omron 2014)
10.45 OV528 Serial Bus Camera System (OmniVision Technologies 2002)
10.46 OV6620/OV6120 Single-Chip CMOS Digital Camera (OmniVision Technologies 1999)
10.47 OV7640/OV7140 CMOS VGA CAMERACHIPS (OmniVision Technologies 2003)
10.48 OV9655/OV9155 (OmniVision Technologies 2006)
10.49 PCF50606/605 Single-Chip Power Management Unit+ (Philips 2002)
10.50 PIC18 Microcontroller Family (Microchip 2000)
10.51 Qimonda HYB18L512160BF-7.5 (Qimonda AG 2006)
10.52 SBT30EDU Sensor and Prototyping Board (EasySen LLC 2008a)
10.53 SBT80 Multi-Modality Sensor Board for TelosB Wireless Motes (EasySen LLC 2008a)
10.54 Spartan-3 FPGA (XILINX 2013)
10.55 Stargate (Crossbow 2004)
10.56 Stargate NetBridge (Crossbow 2007b)
10.57 T-Node (SOWNet 2014)
10.58 TC55VCM208ASTN40,55 CMOS Static RAM (Toshiba 2002)
10.59 Telos (Moteiv 2004)
10.60 TinyNode (Dubois-Ferrière et al. 2006)
10.61 Tmote Connect (Moteiv 2006a)
10.62 Tmote Sky (Moteiv 2006b)
10.63 TSL250R, TSL251R, TSL252R Light-to-Voltage Optical Sensors (TAOS 2001)
10.64 WiEye Sensor Board for Wireless Surveillance and Security Applications (EasySen LLC 2008b)
10.65 WM8950 (Wolfson Microelectronics 2011)
10.66 Xbee/Xbee-PRO OEM RF Modules (MaxStream 2007)
10.67 XC2C256 CoolRunner-II CPLD (XILINX 2007)
10.68 XE1205I Integrated UHF Transceiver (Semtech 2008)
References
Part V: Ignition
Chapter 11: Third Takeoff
Index