Leung Advances in Wireless Sensors and Sensor Networks

1. Auflage 2010
ISBN: 978-3-642-12707-6
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 361 Seiten

In recent times wireless sensors and sensor networks have become a great interest to research, scientific and technological community. Though the sensor networks have been in place for more than a few decades now, the wireless domain has opened up a whole new application spaces of sensors. Wireless sensors and sensor networks are different from traditional wireless networks as well computer networks and therefore pose more challenges to solve such as limited energy, restricted life time, etc. This book intends to illustrate and to collect recent advances in wireless sensors and sensor networks, not as an encyclopedia but as clever support for scientists, students and researchers in order to stimulate exchange and discussions for further developments.

Autoren/Hrsg.

Leung, Henry

Inhaltsverzeichnis

1;Guest Editorial;5
2;Table of Contents;7
3;Security for Wireless Sensor Networks – Configuration Aid;9
3.1;Introduction;9
3.2;Motivation;10
3.3;Threats to Wireless Sensor Networks;10
3.4;Security Primitives;14
3.5;Security Protocols;17
3.6;Configuring Security;20
3.6.1;Protocols Supported;22
3.6.2;Application Based Parameters;23
3.6.3;Configuration Scenario;27
3.7;Tool Design;28
3.8;Conclusion;29
3.9;References;30
4;Low Power Wireless Buoy Platform for Environmental Monitoring;33
4.1;Introduction;33
4.2;WSN for Habitat Monitoring;36
4.3;Buoys for Environmental Monitoring;37
4.4;System Design;39
4.5;Networking;40
4.6;Time and Network Synchronization;41
4.7;Power Supply;42
4.8;Temperature Profiling;43
4.9;Transmission Range Tests;44
4.10;Buoy Deployment;46
4.11;Results and Conclusion;46
4.12;Ongoing and Future Work;47
4.13;References;50
5;A Detail Performance Evaluation of the Novel Mechanisms Ensuring Maximum Connectivity and Data Transmission between Nodes, Based on the Heuristics Under 5-Color Clustered Response Approach;51
5.1;Introduction;52
5.2;Related Work on All Layers of Protocol Stack;53
5.3;Literature Review of Topology Discovery Mechanisms;54
5.4;5-Color Clustered Response Mechanism;54
5.4.1;Heuristics Behind 5-Color Clustered Response Mechanism;57
5.5;A Novel Duty Cycle Assignment Mechanism;58
5.5.1;Phase 1 (Determining Recommended Number of Gray Nodes);58
5.5.2;Phase 2 (Node Selection Mechanism for a Parent Node);60
5.5.3;Phase 3 (Node Selection Mechanism for a Child Black Node);61
5.5.4;Transmission Mechanisms of Information Packets between a Pair of Clusters;62
5.6;Overview of the Scenarios When a Node Fails;62
5.7;Fault Tolerance Mechanism;63
5.7.1;Discussion of Fault Tolerance Mechanism at Faulty State of a Node;63
5.7.2;Discussion of Fault Tolerance Mechanism at the Operational State of a Node;68
5.7.3;Method of Resetting the States of the Nodes;68
5.7.4;Fault Tolerance Mechanism When a Node Fails Multiple Times;69
5.7.5;Mechanism of Caching Packets Transmitted to a Faulty Node;69
5.8;Performance Evaluation;69
5.8.1;Experiment 1;69
5.8.2;Experiment 2;70
5.8.3;Experiment 3;71
5.8.4;Experiment 4;72
5.8.5;Experiment 5;72
5.8.6;Experiment 6;72
5.8.7;Experiment 7;73
5.8.8;Experiment 8;74
5.8.9;Experiment 9;75
5.8.10;Experiment 10;75
5.8.11;Experiment 11;76
5.8.12;Experiment 12;77
5.8.13;Experiment 13;77
5.9;Conclusion and Future Work;78
5.10;References;79
6;Inventory Management in the Packaged Gas Industry Using Wireless Sensor Networks;82
6.1;Asset Tracking and the Packaged Gas Industry;82
6.2;Using Wireless Sensor Networks for Gas Cylinder Tracking;87
6.3;Prototype System Implementation;89
6.3.1;System Overview;89
6.3.2;System Operation;90
6.3.3;Key Features;92
6.3.4;Sensors for Asset Tracking;97
6.4;Experimental Results;98
6.4.1;Battery Life;98
6.4.2;Using the Hand Held Unit for Asset Discovery;100
6.4.3;Industrial Demonstration;101
6.5;Conclusions and Future Work;103
6.6;References;105
7;An EM-IMM Method for Simultaneous Registration and Fusion of Multiple Radars and ESM Sensors;108
7.1;Introduction;108
7.2;Sensor Network Model for Radars and ESM Sensors;110
7.3;Simultaneous Registration and Fusion Using EM-IMM ;113
7.3.1;E-STEP;114
7.3.2;Expectation Evaluation by IMM Filter;115
7.3.3;M-STEP;117
7.4;Bias Analysis of the EM Method;119
7.5;Performance Evaluation by PCRB;122
7.6;Simulation Results;126
7.7;Conclusions;129
7.8;References;129
8;Locatable, Sensor-Enabled Multistandard RFID Tags;132
8.1;Introduction;132
8.2;System Architecture;133
8.3;Technology Aims;134
8.4;Analog Multistandard Frontend;135
8.4.1;Ultra Low-Power Rectification;135
8.4.2;Tag-to-Reader Communication;138
8.4.3;Reader-to-Tag Communication;139
8.5;Digital Baseband Processing;140
8.5.1;Energy Efficiency of Digital Circuits;141
8.6;Localization of UHF Labels;142
8.6.1;Distance Measurement with FMCW Radar;143
8.6.2;Common Requirements for the Oscillator;145
8.6.3;Distance Calculation with Digital Signal Processing;147
8.7;Integrated Sensors and Their Interface;149
8.7.1;Analog to Digital Converter;150
8.7.2;Input Multiplexer;152
8.7.3;Temperature Sensor;152
8.7.4;Measurements;153
8.8;Summary;154
8.9;References;155
9;Optimal Sensor Network Configuration Based on Control Theory;158
9.1;Optimal Sensor Network Configuration Considering Estimation Error Variance and Communication Energy ;158
9.1.1;Introduction;158
9.1.2;Problem Formulation;159
9.1.3;Estimation Algorithm;162
9.1.4;Sensor Scheduling Algorithm;166
9.1.5;Experimental Evaluation;168
9.1.6;Conclusions;170
9.2;Optimal Sensor Network Configuration via Multi-hop Communication;170
9.2.1;Introduction;170
9.2.2;Problem Formulation;172
9.2.3;Proposed Method;174
9.2.4;Network Configuration Algorithm;177
9.2.5;Experimental Evaluation;178
9.2.6;Conclusion;180
9.3;References;182
10;Optimal Local Map Registration for Wireless Sensor Network Localization Problems;184
10.1;Introduction;184
10.2;Related Work;187
10.3;Optimal Local Map Registration;189
10.3.1;The Optimal Rotation Matrix;192
10.3.2;Global Map Construction;195
10.4;Performance Analysis;195
10.5;Conclusions;201
10.6;Appendix;204
11;Wireless Sensor Network: Application to Vehicular Traffic;206
11.1;Introduction;206
11.2;Background;207
11.2.1;Vehicle Sensor Review;208
11.2.2;Wireless Sensor Network;209
11.3;System and Network Architecture;210
11.3.1;System Architecture;210
11.3.2;Network Topology;211
11.3.3;Antenna;212
11.4;Protocols;213
11.4.1;Mac Protocol;213
11.4.2;Frame Format;214
11.4.3;Frame Format between Server Node and PC (Data Server);216
11.5;Data Analysis;217
11.5.1;Preprocess the Data;217
11.5.2;Vehicle Detection;218
11.5.3;Estimation of Vehicle's Speed and Length;218
11.5.4;Averaged Magnetic Energy;219
11.5.5;Hill Pattern Peaks;220
11.6;Experiments and Results;220
11.6.1;Detectability, Length and Speed Estimation Experiment;220
11.6.2;RF Communications Experiment;221
11.6.3;Classification Experiment;222
11.7;Conclusion;226
11.8;References;227
12;Thermal Energy Harvesting for Wireless Sensor Nodes with Case Studies;228
12.1;Introduction;228
12.2;Conversion Methods and Technologies;230
12.2.1;Thermoelectric;230
12.2.2;Thermionic and Thermo-tunnelling;232
12.2.3;Power Management Systems;233
12.3;Heat Sources and Applications;233
12.3.1;Solar;233
12.3.2;Ground to Ambient Air;234
12.3.3;Water to Ambient Air;236
12.3.4;Transport;236
12.3.5;Industrial Waste Heat;237
12.3.6;The Human Body;238
12.4;Case Study: The Use of Environmental Heat;238
12.4.1;Experimental Details;240
12.4.2;Experimental Variations;240
12.4.3;Experimental Results;241
12.5;Conclusion;247
12.6;References;247
13;IEEE 1451.5 Standard-Based Wireless Sensor Networks;250
13.1;Introduction;250
13.2;Related Work;253
13.3;IEEE 1451.5 Standard-Based Wireless Sensor Networks;257
13.3.1;Architecture of IEEE 1451.5 Standard-Based Wireless Sensor Networks;257
13.3.2;IEEE 1451.5 Standard-Based Wireless Sensor Networks;267
13.4;Service-Oriented and IEEE 1451.5-802.11 Standard-Based Wireless Sensor Network;270
13.4.1;Service-Oriented and IEEE 1451.5-802.11 Standard-Based Wireless Sensor Network;270
13.4.2;Case Studies;272
13.5;Summary;276
13.6;References;276
14;Fuzzy Based Optimized Routing Protocol for Wireless Sensor Networks;279
14.1;Introduction;279
14.2;Related Work;280
14.3;Proposed Routing Protocol;281
14.3.1;Protocol Operation;281
14.4;Performance Analysis and Simulation Results;285
14.5;Conclusions;287
14.6;References;287
15;Energy Aware Sensor Group Scheduling to Minimise Estimated Error from Noisy Sensor Measurements;289
15.1;Introduction;289
15.2;Chapter Overview;290
15.3;Problem Description and Formulation;291
15.4;System Models;292
15.4.1;State Estimation;293
15.4.2;Error Cost Function;295
15.5;Scheduling Methodologies;296
15.5.1;Dynamic Programming;296
15.5.2;Particle Swarm Optimisation;298
15.5.3;One-Step-Look-Ahead Method;301
15.6;Experiment and Simulation Results;301
15.7;Conclusion;309
15.8;References;309
16;Smart Home for Elderly Using Optimized Number of Wireless Sensors;312
16.1;Introduction;312
16.2;Motivation – Need for Early Detection of Aging Changes;314
16.3;Literature Survey;314
16.4;Wireless Sensors Based In-home Monitoring Using Optimized Number of Sensors;316
16.5;System Description;317
16.5.1;Electrical Appliance Monitoring Unit;319
16.5.2;Water-Use Monitoring Unit;319
16.5.3;Bed Monitring Unit;321
16.5.4;Emergency Button;326
16.5.5;The Cellular Modem;326
16.5.6;Radio Frequency Communication Protocol;327
16.5.7;Interface and Control Software;328
16.6;Experimental Results;329
16.7;Conclusions and Future Work;332
16.8;References;332
17;Estimation of Packet Error Rate at Wireless Link of VANET;334
17.1;Introduction;334
17.1.1;Related Works;335
17.1.2;Our Works;337
17.2;Measurement of Packet Error Rate in Vanet;338
17.3;Analysis of Packet Error in VANET;340
17.3.1;Burst Length and Gas Length;340
17.3.2;Statistical Properties of Packet Error Rate;342
17.4;Estimation of Packet Error Rate in VANET;347
17.4.1;Estimate Per Using Plm;348
17.4.2;Packet Error Rate Estimation Using Rpee;353
17.5;Conclusions;362
17.6;References;362
18;Author Index;365

Leseproben

"Wireless Sensor Network: Application to Vehicular Traffic (p. 199-200)

Abstract.

In this paper we are reporting our current development of wireless sensor network to e?ectively monitor vehicular tra?c. A simple star con?guration that consists of a server node communicating with a number of sensor nodes is proposed because of its low complexity, and easy and quick deployment, maintenance and relocation. Our system consists of the sensor, processor, and RF transceiver. We choose the magneto-resistive sensor to detect vehicles as it yields high accuracy with small size. The sensor yields important vehicle informations such as vehicle count, speed, and classi?cation.

The network topology is a simple star network. Two Medium Access Communication Protocols (MAC) are analyzed and can be automatically switched based on two di?erent tra?c scenarios. An antenna design is shown to ?t with a small sensor node. Experiments show that the proposed system yields good data processing results. The classi?cation of vehicles is very promising for major types of vehicles: motorcycle, small vehicle, and bus. RF communications is employed that cable installation can be avoided. Protocol frame formats are provided for both RF communications and RS232. This protocol is very simple and can be easily extended when new sensors or new data types are available.

1 Introduction

Tra?c congestion is all big city’s major concern. It hinders substantial economic and social growth. Work have been proposed with the common goal of alleviating tra?c congestion. It is widely agreed that e?cient tra?c planning and management often reduce the congestion to a certain degree. Let’s take Bangkok for instance. During rush hours, the city often manages the traf- ?c by resorting to the tra?c police as a conventional and common practice.

Police o?cers are dispatched to major streets and junctions to help direct and control tra?c ?ow. The tra?c police manually switched tra?c lights at these junctions based on real-time tra?c conditions being observed and communicated among them over their trunked-radio. The lack of tra?c data or wrong tra?c information will worsen the tra?c situation. This particular situation clearly shows that tra?c data collection is very important for an e?ective real-time tra?c management. It is thus important that an e?cient management of tra?c requires data collection process in the ?rst step. A number of work have studied on various vehicle sensors, their accuracy in collecting data, and their operation and functionality.

These sensors include inductive loop [1,8,5], optical sensor [13,8], ultrasonic [6,8], and magnetic sensor [7,8]. Applications of these sensors to tra?c data collection and processing have been numerously proposed [11,13,6,12,2,14,5,9]. Most of the tra?c data collecting devices require signal and power cables. Recently, wireless sensor network has been applied to traf- ?c monitoring systems as it yields several advantages including quick deployment and maintenance, less cables involved, and small size [9,4]. Therefore, applications of wireless sensor network to tra?c data collection are numerous."