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E-raamat: Wireless Sensor Networks: A Cognitive Perspective

(Queen's University, Kingston, Ontario, Canada)
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"Preface Every day, Wireless Sensor Networks are gaining in popularity and applications. They are now widely used and deployed in key areas such as smart homes and buildings, intelligent transportation, health care, public health, military, food safety, water quality, smart power grid, industrial processes, precision agriculture, security, environment, etc. Various types of data with different rates and requirements are to be transmitted through these networks. Users are always in need for better coverage, connectivity, security and energy efficiency while looking for miniaturized, low-cost, and autonomous devices. Due to these requirements, classical techniques used in this field will soon reach their limitations and will no longer fulfill users' requirements. Cognitive communications is a new concept that emerged few years ago and has proven its importance, especially in the field of Cognitive Radio. This concept has been generalized to cover all aspects of the wireless communications system design. Wireless sensor networks represent an excellent area where cognition and intelligence can be easily developed and exploited not only to benefit the network efficiency and user requirements, but also to create new needs and new applications. This is because wireless sensor networks are, by nature, distributed systems where information can be made available about "everything" in the deployment area. Information may include user needs, user requirements, environment conditions, network conditions, node-level information (such as battery level, transmission range, processing capabilities, position information), etc. Security is also an important issue wherethe cognitive concept can play a key role"--



With classical techniques for data transmission soon reaching their limitations, cognitive approaches may offer a solution to user requirements for better coverage, connectivity, security, and energy efficiency at lower cost. Wireless Sensor Networks: A Cognitive Perspective presents a unified view of the state of the art of cognitive approaches in telecommunications. A benchmark in the field, it brings together research that has previously been scattered throughout conference and journal papers.

Cutting-Edge Topics in Cognitive Communications

After a review of the cognitive concept and approaches, the book outlines a generic architecture for cognition in wireless sensor networks. It then targets specific issues that need to be addressed through cognition, from cognitive radio and spectrum access to routing protocols. The book also explores how to use weighted cognitive maps to improve network lifetime through optimizing routing, medium access, and power control while fulfilling end-to-end goals. The final chapter discusses the implementation of hardware for GPS/INS-enabled wireless sensor networks. This addresses an important need for real-time node position information in many wireless sensor network applications and communication protocols.

Real-World Applications of Wireless Sensor Networks using the Cognitive Concept

Written in a tutorial style, the book supplies an in-depth survey of each topic, accompanied by detailed descriptions of the algorithms and protocols. It also provides a step-by-step analysis of the various communications systems through extensive computer simulations and illustrations. Examples cover environmental monitoring, vehicular communications, tracking, and more. A comprehensive overview of cognitive communications in wireless sensor networks, this work lays the foundations for readers to participate in a new era of research in this emerging field.

Preface ix
Acronyms xiii
1 Introduction to Cognitive Approaches in Wireless Sensor Networks
1(22)
1.1 Introduction
1(4)
1.1.1 Application Layer Requirements
3(1)
1.1.2 Physical Layer Constraints and Requirements
4(1)
1.1.3 Network Status Sensors
4(1)
1.2 Related Work
5(7)
1.2.1 Knowledge Plane and Cognitive Networks
6(2)
1.2.2 Cognitive Techniques Used in Sensor Networks
8(4)
1.3 A Generic Architecture for Cognitive Wireless Sensor Networks
12(8)
1.3.1 ZigBee Stack
15(1)
1.3.2 Network Status Sensors
15(1)
1.3.3 Inputs from the Physical Layer
15(1)
1.3.4 Change Monitoring Engine
16(1)
1.3.5 Knowledge Base
16(1)
1.3.6 Cognitive Decision-Making Engine
17(1)
1.3.7 Optimization Engine
18(1)
1.3.8 Interaction among the Cognitive Components
18(2)
1.4 Conclusion
20(3)
References
21(2)
2 Cognitive Radio Networks and Dynamic Spectrum Access
23(28)
2.1 Introduction
23(3)
2.1.1 History of Cognitive Radio
23(1)
2.1.2 MIMO and Cooperative Diversity Techniques
24(2)
2.2 Spectrum Awareness
26(9)
2.2.1 Spectrum Sensing Challenges
28(2)
2.2.2 Spectrum Sensing Methods
30(5)
2.3 Cooperative Sensing
35(4)
2.3.1 Narrowband Cooperative Sensing
35(3)
2.3.2 Wideband Cooperative Sensing
38(1)
2.4 Dynamic Spectrum Access
39(7)
2.4.1 MIMO Systems for Spectrum Access
41(3)
2.4.2 Cooperative Spectrum Access
44(2)
2.5 Conclusion
46(5)
References
46(5)
3 Adaptive Modulation, Adaptive Power Allocation, and Adaptive Medium Access
51(50)
3.1 Introduction
51(2)
3.2 System Model
53(2)
3.2.1 Information Source and Sink
53(1)
3.2.2 Transmitter
53(1)
3.2.3 Receiver
54(1)
3.2.4 Wireless Channel
54(1)
3.2.5 Lognormal Shadowing Channel Model
54(1)
3.2.6 Rician Fading Channel Model
55(1)
3.3 Adaptive Transmission and Feedback Communication System
55(8)
3.3.1 Introduction
55(1)
3.3.2 Adaptive System Design
55(1)
3.3.3 Link Adaptations
56(1)
3.3.4 Link Adaptation for Energy-Constrained Networks
57(1)
3.3.5 Adaptive Techniques
58(5)
3.4 Multihop Relay Network and Energy-Constrained Network Analysis
63(10)
3.4.1 Energy Consumption with Adaptation Techniques
63(1)
3.4.2 Single-Hop Discrete Rate Continuous Power Adaptation
64(1)
3.4.3 Multihop Relay Networks
65(4)
3.4.4 MAC Layer Adaptive Modulation and Adaptive Sleep
69(4)
3.5 Simulation Examples and Illustrations
73(23)
3.5.1 Simulation Objective
73(1)
3.5.2 Energy Optimization
74(14)
3.5.3 Power Control Adaptation Policies
88(1)
3.5.4 TWo-Link Relay Network Adaptation
88(4)
3.5.5 Performance of Commercial WSN Nodes
92(4)
3.6 Conclusions
96(5)
References
97(4)
4 Cross-Layer Approaches to QoS Routing in Wireless Multihop Networks
101(40)
4.1 Introduction
101(2)
4.2 Design Challenges and Considerations
103(5)
4.2.1 QoS Metrics
103(1)
4.2.2 Design Challenges
104(2)
4.2.3 Network Resources and Performance Metrics
106(2)
4.3 Taxonomy of QoS Routing Protocols in Multihop Networks
108(20)
4.3.1 QoS Routing in MANETs
108(6)
4.3.2 QoS Routing in WMNs
114(3)
4.3.3 QoS Routing in VANETs
117(6)
4.3.4 QoS Routing in WSNs
123(4)
4.3.5 Limitations to Routing Design across Different Networks
127(1)
4.4 Comparison between QoS Routing Protocols
128(5)
4.5 Challenges and Future Directions
133(3)
4.6 Conclusions
136(5)
References
136(5)
5 Cognitive Diversity Routing
141(52)
5.1 Overview of Routing Protocols in Wireless Sensor Networks
141(8)
5.1.1 Wireless Sensor Network Routing Protocols
141(3)
5.1.2 Energy-Aware Protocols
144(3)
5.1.3 Diversity Routing
147(1)
5.1.4 Cognitive Protocols
148(1)
5.2 System Models
149(4)
5.2.1 The Propagation Model
150(1)
5.2.2 Network Lifetime
151(2)
5.3 Cognitive Diversity Routing
153(8)
5.3.1 Cognitive Diversity Routing Methodology
153(3)
5.3.2 Implementation in OPNET Modeler 15.0
156(1)
5.3.3 Pseudo-Code for Cognitive Diversity Routing
157(4)
5.4 Priority Node Selection
161(2)
5.5 Performance Evaluation
163(26)
5.5.1 Grid Deployment
165(7)
5.5.2 Deployment with Forced Path
172(2)
5.5.3 Random Deployment
174(1)
5.5.4 Node Density and Scalability
175(6)
5.5.5 Optimization
181(1)
5.5.6 Giving Emphasis to the Channel Profile
182(7)
5.6 Conclusion
189(4)
References
190(3)
6 Enabling Cognition through Weighted Cognitive Maps
193(28)
6.1 Introduction
193(1)
6.2 Related Work
194(1)
6.3 Fundamentals of WCM
195(3)
6.4 Designing WCMs to Achieve Cognition in WSNs
198(11)
6.4.1 Designing a WCM for Transmit Power, Data Rate, and Duty Cycle Adaptation
199(4)
6.4.2 Designing a WCM to Guarantee Connectivity and Coverage
203(2)
6.4.3 Designing a WCM for Congestion Control
205(2)
6.4.4 End-to-End Goal and the Overall WCM
207(2)
6.5 Simulation Results
209(9)
6.5.1 Evaluation Using Uniform Random Topology
210(4)
6.5.2 Evaluation Using Bottleneck Paths
214(2)
6.5.3 Complexity of the System
216(2)
6.6 Conclusions
218(3)
References
218(3)
7 Hardware Architecture for GPS/INS-Enabled Wireless Sensor Networks
221(26)
7.1 Introduction
221(3)
7.2 Hardware Implementation
224(4)
7.2.1 GPS and INS Data Acquisition
224(1)
7.2.2 Navigation Data Processing
225(1)
7.2.3 Power Management MCU
226(1)
7.2.4 Wireless Radio Frequency Transceiver
226(1)
7.2.5 Power Supply
227(1)
7.3 System Software Design
228(6)
7.3.1 System Initialization
228(2)
7.3.2 System Power Management
230(1)
7.3.3 DSP Memory Allocation
231(1)
7.3.4 ZigBee Node Software Design
232(2)
7.4 Test Results
234(10)
7.4.1 Equipment and Setup
234(2)
7.4.2 Real-Time Performance Analysis
236(1)
7.4.3 Random Error Modeling
237(2)
7.4.4 Open Field Tests
239(5)
7.5 Conclusion
244(3)
References
245(2)
Index 247
Dr. Mohamed Ibnkahla is an associate professor in the Electrical and Computer Engineering Department at in Queens University, Canada. He is currently leading several projects applying wireless sensor networks to several areas such as forest monitoring, wildlife and species at risk tracking, smart grid, drinking water monitoring, food traceability, intelligent transportation systems, and sustainable communities.
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