Wireless Sensor Systems for Extreme Environments E-Book
108,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Provides unique coverage of wireless sensor system applications in space, underwater, underground, and extreme industrial environments in one volume
This book covers the challenging aspects of wireless sensor systems and the problems and conditions encountered when applying them in outer space, under the water, below the ground, and in extreme industrial environments. It explores the unique aspects of designs and solutions that address those problems and challenges, and illuminates the connections, similarities, and differences between the challenges and solutions in those various environments.
The creation of Wireless Sensor Systems for Extreme Environments is a response to the spread of wireless sensor technology into fields of health, safety, manufacturing, space, environmental, smart cities, advanced robotics, surveillance, and agriculture. It is the first of its kind to present, in a single reference, the unique aspects of wireless sensor system design, development, and deployment in such extreme environments—and to explore the similarities and possible synergies between them. The application of wireless sensor systems in these varied environments has been lagging dramatically behind their application in more conventional environments, making this an especially relevant book for investigators and practitioners in all of these areas.
Wireless Sensor Systems for Extreme Environments is presented in five parts that cover:
- Wireless Sensor Systems for Extreme Environments—Generic Solutions
- Space WSS Solutions and Applications
- Underwater and Submerged WSS Solutions
- Underground and Confined Environments WSS Solutions
- Industrial and Other WSS Solutions
This book is a welcome guide for researchers, post-graduate students, engineers and scientists who design and build operational and environmental control systems, emergency response systems, and situational awareness systems for unconventional environments.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 859
Veröffentlichungsjahr: 2017
Ähnliche
Table of Contents
Cover
Title Page
Copyright
Dedication
List of Contributors
Preface
Part I: Wireless Sensor Systems for Extreme Environments-Generic Solutions
Chapter 1: Wireless Sensor Systems for Extreme Environments
1.1 Introduction
1.2 Wireless Sensor Systems for Space and other Extreme Environments
1.3 Chapter Abstracts
Reference
Chapter 2: Feedback Control Challenges with Wireless Networks in Extreme Environments
2.1 Introduction
2.2 Controllers in Extreme Environments
2.3 System Dynamics and Control Design Fundamentals
2.4 Feedback Control Challenges when using Wireless Networks
2.5 Effect of Delay on the Transient Response of a Second-order System
2.6 Discussion
2.7 Summary
References
Chapter 3: Optimizing Lifetime and Power Consumption for Sensing Applications in Extreme Environments
3.1 Introduction
3.2 Overview and Technical System Description
3.3 Power and Lifetime Optimization
3.4 Visualization and Numerical Results
3.5 Application of Power Control in Extreme Environments
3.6 Summary
References
Chapter 4: On Improving Connectivity-based Localization in Wireless Sensor Networks
4.1 Introduction
4.2 Connectivity-based Localization in One-hop Networks
4.3 Connectivity-based Localization in Multi-hop Networks
4.4 On Improving Connectivity-based Localization
4.5 Summary
References
Chapter 5: Rare-events Sensing and Event-powered Wireless Sensor Networks
5.1 Coverage Preservation [19]
5.2 Event-powered Wireless Sensor [20]
5.3 Cluster-Centric WSNs for Rare-event Monitoring [21]
5.4 Summary
References
Part II: Space WSS Solutions and Applications
Chapter 6: Battery-less Sensors for Space
6.1 Introduction
6.2 Wired or Wireless Sensing: Cost–Benefit Analysis
6.3 Active and Passive Wireless Sensors
6.4 Design Considerations for Battery-less Sensors
6.5 Summary
References
Chapter 7: Contact Plan Design for Predictable Disruption-tolerant Space Sensor Networks
7.1 Introduction
7.2 Contact Plan Design Methodology
7.3 Contact Plan Design Analysis
7.4 Contact Plan Design Discussion
7.5 Summary
References
Chapter 8: Infrared Wireless Sensor Network Development for the Ariane Launcher
8.1 Introduction
8.2 Development Processes and Measurements of Infrared Transceiver ASIC
8.3 Summary
References
Chapter 9: Multichannel Wireless Sensor Networks for Structural Health Monitoring
9.1 Context
9.2 General Multichannel Challenges
9.3 Multichannel Challenges for Data Gathering Support
9.4 Sahara: Example of Solution
9.5 Summary
Acknowledgments
References
Chapter 10: Wireless Piezoelectric Sensor Systems for Defect Detection and Localization
10.1 Introduction
10.2 Lamb Wave-based Defect Detection
10.3 Wireless PZT Sensor Networks
10.4 Wireless PZT Sensor Node
10.5 Distributed Data Processing
10.6 Summary
Conflict of Interests
Acknowledgment
References
Chapter 11: Navigation and Remote Sensing using Near-space Satellite Platforms
11.1 Background and Motivation
11.2 Near-space Platforms in Wireless Sensor Systems
11.3 Overview of NSPs in Wireless Sensor Systems
11.4 Integrated Wireless Sensor Systems
11.5 Arrangement of Near-space Platforms
11.6 Limitations and Vulnerabilities
11.7 Summary
References
Part III: Underwater and Submerged WSS Solutions
Chapter 12: Underwater Acoustic Sensing: An Introduction
12.1 Introduction
12.2 Underwater Wireless Smart Sensing
12.3 Netted Sensors
12.4 Networking
12.5 Typical Underwater Sensing Applications
12.6 Summary
References
Chapter 13: Underwater Anchor Localization Using Surface-reflected Beams
13.1 Introduction
13.2 UREAL Angle of Arrival Measurements
13.3 Closed-form Least Squares Position Estimation
13.4 Prototype Evaluation
13.5 Summary
References
Chapter 14: Coordinates Determination of Submerged Sensors with a Single Beacon Using the Cayley–Menger Determinant
14.1 Introduction
14.2 Underwater Wireless Sensor Networks
14.3 Dynamicity of Underwater Environment
14.4 Proposed Configuration
14.5 Distance Determination
14.6 Coordinate Determination
14.7 Simulation Results
14.8 Summary
References
Chapter 15: Underwater and Submerged Wireless Sensor Systems: Security Issues and Solutions
15.1 Introduction
15.2 Underwater Wireless Sensor Systems
15.3 Security Requirements, Issues and Solutions
15.4 Future Challenges and Research Directions
15.5 Summary
References
Part IV: Underground and Confined Environments WSS Solutions
Chapter 16: Achievable Throughput of Magnetic Induction Based Sensor Networks for Underground Communications
16.1 Introduction
16.2 Throughput Maximization for MI-WUSNs
16.3 Results
16.4 Discussion
16.5 Summary
References
Chapter 17: Agricultural Applications of Underground Wireless Sensor Systems: A Technical Review
17.1 Introduction
17.2 WSN Technology in Agriculture
17.3 WSNs for Agriculture
17.4 Design Challenges of WSNs in Agriculture
17.5 WSN-based Applications in Agriculture
17.6 Summary
References
Part V: Industrial and Other WSS Solutions
Chapter 18: Structural Health Monitoring with WSNs
18.1 Introduction
18.2 SHM Sensing Techniques
18.3 WSN-enabled SHM Applications
18.4 Network Topology and Overlays
18.5 Summary
Acknowledgment
References
Chapter 19: Error Manifestations in Industrial WSN Communications and Guidelines for Countermeasures
19.1 Introduction
19.2 Compromising Factors in IWSN Communication
19.3 The Statistics of Link-quality Metrics for Poor Links
19.4 The Statistical Properties of Bit- and Symbol-Errors
19.5 Guidelines for Countermeasures
19.6 Summary
References
Chapter 20: A Medium-access Approach to Wireless Technologies for Reliable Communication in Aircraft
20.1 Introduction
20.2 Reliability Assessment Framework
20.3 Metrics and Parameters
20.4 Candidate Wireless Technologies
20.5 Evaluation
20.6 Summary
References
Chapter 21: Applications of Wireless Sensor Systems for Monitoring of Offshore Windfarms
21.1 Introduction
21.2 Literature Review
21.3 WSNs in Windfarms
21.4 Simulation and Discussion
21.5 Summary
References
Index
End User License Agreement
Pages
xix
xx
xxi
xxii
xxiii
xxiv
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
111
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
247
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
381
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
Guide
Cover
Table of Contents
Preface
Part I: Wireless Sensor Systems for Extreme Environments-Generic Solutions
Begin Reading
List of Illustrations
Chapter 1: Wireless Sensor Systems for Extreme Environments
Figure 1.1 An illustration of the four harsh, and most difficult areas for using the WSS and its new applications.
Chapter 2: Feedback Control Challenges with Wireless Networks in Extreme Environments
Figure 2.1 Launch vehicle with wirelessly networked strain sensors [11].
Figure 2.2 Elastic mode reconstruction with wirelessly networked strain sensors [12].
Figure 2.3 Mode reconstruction results with no delay between sensors.
Figure 2.4 Mode reconstruction results with relative delay of 0.0015 s between each sensor.
Figure 2.5 Mode reconstruction results with relative delay of 0.015 s between each sensor.
Figure 2.6 Block diagram of a typical feedback control system.
Figure 2.7 First-order transient response.
Figure 2.8 Underdamped transient response.
Figure 2.9 Elementary feedback control system.
Figure 2.10 Delay verification.
Figure 2.11 First-order system with single delay in feedback path.
Figure 2.12 First-order system with multiple delays in the feedback path.
Figure 2.13 Single-sensor feedback loop with AWGN-CD model.
Figure 2.14 Rise time vs noise variance.
Figure 2.15 Rise time versus delay.
Figure 2.16 Overshoot versus noise variance.
Chapter 3: Optimizing Lifetime and Power Consumption for Sensing Applications in Extreme Environments
Figure 3.1 A distributed sensor network.
Figure 3.2 System model of the distributed sensor network.
Figure 3.3 Reference bars for comparisons with other simulations. The values for the minimum overall power consumption are and , and and the expected number of active SNs in each observation step is 4.
Figure 3.4 Decreasing worsens the lifetime, the power consumption, and the convergence speed between both optimization results. The values of the minimum overall power consumptions are and , and the expected number of active SNs in each observation step is 7.
Figure 3.5 Decreasing can worsen lifetime, power consumption, and convergence speed. The values for the minimum overall powerconsumptions are and , and the expected number of active SNs in each observation step is 4.
Figure 3.6 Decreasing always worsens both the lifetime and the power consumption, but in contrast it increases the convergence speed. The values for the minimum overall power consumption are and , and the expected number of active SNs in each observation step is 6.
Figure 3.7 Reference bars for comparisons with other simulations. The values for the minimum overall power consumptions are and , and the expected number of active SNs in each observation step is 4.
Figure 3.8 Decreasing worsens the lifetime, the power consumption, and the convergence speed between both optimization results. The values for the minimum overall power consumptions are and , and the expected number of active SNs in each observation step is 7.
Figure 3.9 Decreasing can worsen the lifetime, the power consumption, and the convergence speed. The values for the minimum overall power consumptions are and , and the expected number of active SNs in each observation step is 4.
Figure 3.10 Decreasing always worsens both the lifetime and the power consumption, but in contrast it increases the convergence speed. The values for the minimum overall power consumptions are and , and the expected number of active SNs in each observation step is 6.
Chapter 4: On Improving Connectivity-based Localization in Wireless Sensor Networks
Figure 4.1 Illustration of the DV-hop algorithm.
Figure 4.2 The hop-distance ambiguity problem [26].
Figure 4.3 Perfect-hopping scenario and its geometric relation [26].
Figure 4.4 Comparison of the normalised localization error and the number of deployed nodes [26].
Figure 4.5 The neighborhood distance model: the distance between and is dependent on the number of common neighbors between and [30].
Figure 4.6 Illustration of the shortest-RND path, the shortest-hop path and the shortest-distance path [30].
Figure 4.7 Comparison of average localization error of different connectivity-based localization algorithms [30].
Figure 4.8 Localization error against the value of the PRR threshold [39].
Chapter 5: Rare-events Sensing and Event-powered Wireless Sensor Networks
Figure 5.1 ESCARGO state transitions.
Figure 5.2 Node placements. (a) 1-coverage eligibility; sensing node
A
is not required to provide coverage for sensing node with area of darkest circle. (b) Ideal placement of 10 nodes relative to sensing area; node identifiers shown, sensing area shaded.
Figure 5.3 Stored charge. (a) Mean stored charge during first month of operation (b) Mean stored charge over time by charging efficiency.
Figure 5.4 OSDI and corresponding PGA values.
Figure 5.5 Microcontroller board.
Figure 5.6 Power management board.
Figure 5.7 Base platform.
Figure 5.8 Extension and point mass.
Figure 5.9 Power consumed by the MCB.
Figure 5.10 Charge times versus acceleration.
Figure 5.11 Assembled wireless sensor node inside enclosure (patent pending).
Figure 5.12 Assembled system being installed by Te Papa technicians on Earthquake House.
Figure 5.13 Acceleration over time showing when each packet was sent.
Figure 5.14 Deployment scenario (adapted from a Figure on the Wireless Building Automation website at https://wlba.wordpress.com).
Figure 5.15 State transition diagrams. (a) PAN coordinator (b) Sensor node.
Figure 5.16 Packet arrival time (). (a) IEEE 802.15.4 with uniform random backoff slot selection (b) Cluster-centric MAC with uniform random backoff slot selection (c) IEEE 802.15.4 with geometric random backoff slot selection (d) Cluster-centric MAC with geometric random backoff slot selection.
Figure 5.17 Performance for different network and cluster sizes to transmit all packets: S, standard IEEE 802.15.4 MAC; C, cluster-centric MAC; U, G, uniform/geometric random number generator; cSize, cluster size. (a) Average packet transmission time (b) Average number of retransmissions.
Figure 5.18 Performance in lossy environment (). (a) Total time to transmit all packets (b) Total retransmission attempts.
Figure 5.19 Energy consumption comparison with WSN for SHM proposed by Liu et al. [49], where e
T
(-axis) is the transmission power (mAh).
Chapter 6: Battery-less Sensors for Space
Figure 6.1 Wired sensors inside the wing leading edge of the orbiter Columbia [1].
Figure 6.2 Are wireless sensor networks reliable?
Figure 6.3 IEEE 802.15.4 modified by adding error-correction codes.
Figure 6.4 Multiple correlated observations from simple sensor nodes decoded jointly [3].
Figure 6.5 Joint decoding of correlated sensor data streams.
Figure 6.6 Noisy sensors (white) and faulty sensors (black) in a mesh sensor network.
Figure 6.7 Passive sensor operation principle.
Figure 6.8 Response with 1 (top) and 5 (bottom) interfering sensors.
Figure 6.9 Eliminating interference at source [11].
Chapter 7: Contact Plan Design for Predictable Disruption-tolerant Space Sensor Networks
Figure 7.1 Bundle protocol as an overlay layer with storage to tolerate disruptions.
Figure 7.2 Creating, designing, and implementing contact plans for SSNs.
Figure 7.3 Contact plan design methodology performance.
Figure 7.4 Example of a DTN satellite network: (a) trajectories; (b) modeled with finite state machine; (c) modeled with contact list.
Figure 7.5 Interference generated by ISLs affecting GEO satellites when orbiting over the pole.
Figure 7.6 Satellite architecture: (a) with multiple target nodes; (b) with a power switch; (c) with two transponders; (d) with a steerable antenna.
Figure 7.7 Two possible contact plans: (a) maximum throughput; (b) link fairness.
Figure 7.8 MILP coefficient in objective function.
Figure 7.9 Resulting traffic flow in unconstrained contact topology for .
Figure 7.10 Case study: (a) delivery time and (b) system contact time, for varying
Figure 7.11 TACP contact plan for .
Figure 7.12 TACP contact plan delivery time for and varying fractionation.
Chapter 8: Infrared Wireless Sensor Network Development for the Ariane Launcher
Figure 8.1 Wireless sensor technology placemen and development plan for Ariane 5's upper stage.
Figure 8.2 The electromagnetic radiation/emissions values permitted in the launch vehicle [4].
Figure 8.3 The ARIANE 5ARIANE 5 vehicle equipment bay (VEB) [5].
Figure 8.4 Experimental setup of the infrared transceiver and the target MLI.
Figure 8.5 Bit-error -rate (BER) measurement versus TX diode resistance in different illumination conditions.
Figure 8.6 Bit-error -rate (BER) measurement results with infrared transceiver angle variation.
Figure 8.7 Non- line -of- sight experimental setup with fixed angle and fixed distance.
Figure 8.8 The pPower spectral density comparison between thefor Manchester and unipolar coding.
Figure 8.9 Block diagram for split-phase Manchester coding.
Figure 8.10 Diagram block of the unipolar return zero coding infrared transceiver.
Figure 8.11 The infrared transceiver design layout with 40 input/output pins.
Figure 8.12 The infrared transceiver ASIC.
Figure 8.13 Reliable sensor network with its time-delay distribution.
Figure 8.14 Block diagram of the access point and sensor node.
Figure 8.15 Measurement with VLC and energy distribution through the MLI for time synchronization and time stamping.
Figure 8.16 Architecture of the sensor node.
Figure 8.17 The smart sensors for the telemetry subsystems on the wireless sensor node.
Figure 8.18 Sensor node hardware.
Figure 8.19 The telemetry subsystems measurement sequences.
Figure 8.20 The infrared wireless sensor network tested in the lab.
Chapter 9: Multichannel Wireless Sensor Networks for Structural Health Monitoring
Figure 9.1 Different cases of connectivity.
Figure 9.2 Conflicts with immediate acknowledgment.
Figure 9.3 Examples of topologies.
Figure 9.4 An example topology.
Figure 9.5 Number of packets per second received by the sink. (a) With multiple channels (b) With multiple channels and interfaces.
Figure 9.6 Number of slots used by TMCP, MODESA and optimal values. (a) In configurations (b) In configurations.
Figure 9.7 Buffers needed and slot reuse by TMCP and MODESA. (a) Buffers for TMCP and MODESA (b) Slot reuse by TMCP and MODESA.
Figure 9.8 Number of slots used by TMCP and MODESA and optimal values, for multiple radio interfaces. (a) In configurations (b) In configurations.
Figure 9.9 Topology considered with additional links.
Figure 9.10 Topologies with heterogeneous traffic. (a) Line (b) Multiline (c) Tree.
Figure 9.11 Number of slots used by TMCP and MODESA and optimal values. (a) In configurations (b) In configurations.
Chapter 10: Wireless Piezoelectric Sensor Systems for Defect Detection and Localization
Figure 10.1 Application examples and evolution of PZT sensor networks for SHM: (a) wired PZT sensor network in UAV wing box [16]; (b) high-throughput wireless data acquisition system [17]; (c) next-generation wireless active PZT sensor system.
Figure 10.2 Hanning window modulation: (a) original five cycle 100 kHz sine wave (b) spectrum of five cycle sine wave; (c) Lamb wave after Hanning window modulation; (d) the spectrum after Hanning window modulation.
Figure 10.3 100-kHz Lamb wave and its responses. The signals are normalised for the purposes of illustration.
Figure 10.4 Principles and data processing for PZT-based Lamb wave imaging for SHM: (a) layout of PZT sensors; (b) detected Lamb wave signals; (c) Imaging of defect detection.
Figure 10.5 Topology of a wireless PZT sensor and monitoring network.
Figure 10.6 Block diagram of the wireless PZT node.
Figure 10.7 Prototype of the proposed wireless PZT actuator/sensor node, which consists of two boards: the DSP and conditioning board (bottom) and the RF daughter board (top).
Figure 10.8 Operational overview of the wireless PZT network.
Figure 10.9 Data sampling and collection process: (a) conventional wireless sensor node with low sampling rate; (b) the proposed wireless PZT sensor/actuator node.
Chapter 11: Navigation and Remote Sensing using Near-space Satellite Platforms
Figure 11.1 Near-space definition.
Figure 11.2 Ground coverage area as a function of looking-down angle for different flying altitudes .
Figure 11.3 Typical NSPs designed by NASA. (a) HELIOS (b) Pathfinder
Figure 11.4 Integrated wireless sensor systems with NSP and satellite platforms.
Figure 11.5 Illustration of NSP-terrestrial system.
Figure 11.6 Illustration of standalone NSP system.
Figure 11.7 Geometry of passive NSP-borne receivers and opportunistic spaceborne transmitter.
Figure 11.8 Functional blocks for extracting the reference signal from the direct-path channel for matched filtering the reflected signals.
Figure 11.9 Illustration of NSP observation geometry and coverage region: (a) geometry; (b) triangle coverage; (c) quadrate coverage.
Figure 11.10 Required distance between two NSPs.
Chapter 12: Underwater Acoustic Sensing: An Introduction
Figure 12.1 Typical sources of noise and disturbance that interfere with underwater acoustic signals depending on water depth of the oceans. Seven acoustic devices are indicated: (1) sub-bottom profiler (5 kHz/1 km); (2) modem (20 kHz/1 km); (3) long-baseline positioning (20 kHz/10 km); (4) modem (20 kHz/10 km); (5) depth sounder (40 kHz/1 km); (6) ADCP (75 kHz/500 m); (7) modem (80 kHz/1 km). Source: Toma et al. [1].
Figure 12.2 Illustration of multipath channel with the four eigenrays of interest: DP, RSR, RBR and RSRBR. We propose using RSR and RBR eigenrays for directional communication for NLOS links [7].
Figure 12.3 A seven-element antenna array and the corresponding beamformer pattern, combining the signals of all elements in one axis
Figure 12.4 A hydrophone of stackable multimode piezoelectric directional transducers. Each cylinder is about 50 mm in height with an outer diameter of 108 mm. The horizontal beam pattern is shown; the vertical beam pattern depends on the number of stacks [7].
Figure 12.5 Underwater communicating nodes using acoustic links because of their favourable propagation properties [7].
Figure 12.6 Statistical variation of sound speed in the surface layer and the main thermocline [7].
Figure 12.7 Result of a mathematical simulation results demonstrating how average speed of sound being produced [7].
Figure 12.8 Architecture of a typical application scenario illustrating P2P based UASN. {Drawn based on [30]}
Figure 12.9 A basic core scenario for designing practical applications [32].
Figure 12.10 A geometric 3D observation model. Thirteen directional sensors are in use (S1–S13): S1 and four surrounding sensors (S2–S5) are on the head. The four remaining sensors (S6–S9) are similar to (S10–S13) but located on the other side of the ship [44].
Figure 12.11 Seascape acoustic environment. Courtesy of NeXOS, a collaborative project funded by the European Commission [47].
Chapter 13: Underwater Anchor Localization Using Surface-reflected Beams
Figure 13.1 Network model of the proposed scheme. UREAL uses both line-of-sight (LOS) and non-line-of-sight (NLOS) AOA range information to locate an LD node that has drifted away from the ASN. The water surface function is used for NLOS position estimation.
Figure 13.2 An anchor-free localization scheme used to locate the GP nodes. The located BS and GP nodes will then be used as reference nodes to locate an LD node that has drifted away from the ASN.
Figure 13.3 LOS position estimation, showing the collection of angles from each sensor node.
Figure 13.4 4 NLOS position estimation uses the reflection points as reference to solve for LD position
Figure 13.5 Experimental setup with a tank, wave pump and 3D camera. The projected view of the 3D Kinect sensor onto the tarp is used to create a scaled 3D underwater environment.
Figure 13.6 Localization performance as we vary the AOA variance: (a) for LOS operation (b) for NLOS operation. Results depict low localization error when we have enough reference nodes. The NLOS performance is better than LOS since the number of reflection points exceeds the true number of reference nodes.
Figure 13.8 Combined localization error for varying number of reference nodes.
Figure 13.7 Localization error projection over time: left, sampled water surface obtained from the 3D camera; top right, projected effects of the water surface roughness on the localization error. bottom right, localization error per frame, which coincides with changing RMS roughness.
Chapter 14: Coordinates Determination of Submerged Sensors with a Single Beacon Using the Cayley–Menger Determinant
Figure 14.1 Configuration consisting of one beacon and submerged sensors.
Figure 14.2 Solvable subset configuration with a mobile beacon and three submerged sensors
Figure 14.3 Message transmission for distance determination.
Figure 14.4 Acoustic speed profile according to the Mackenzie equation
Figure 14.5 Average speed with 20 °C surface temperature for a200-m water column
Figure 14.6 Parallel states scenario
Figure 14.7 Subset of three sensors and a mobile beacon for coordinates determination.
Figure 14.8 Positional errors with 10 m circular orientation with Euclidean distances.
Figure 14.9 Calculated sensor positions with respect to actual coordinates.
Figure 14.10 Distance error for sensor S
1
.
Figure 14.12 Distance error for sensor S
3
.
Chapter 15: Underwater and Submerged Wireless Sensor Systems: Security Issues and Solutions
Figure 15.1 Time evolution of the position of one hundred sensors randomly released in a square 4 km on each side [7].
Figure 15.2 Network design of a hierarchical UWSS [39].
Figure 15.3 Security requirements, problems and related solutions.
Figure 15.4 Node communication process.
Figure 15.5 Connectivity for nomadic mobility model [39]
Figure 15.6 Connectivity for meandering mobility model [39]
Chapter 16: Achievable Throughput of Magnetic Induction Based Sensor Networks for Underground Communications
Figure 16.1 Block diagram of MI waveguide with transmitter, receiver, and relays.
Figure 16.2 Signal propagation in MI waveguide based WUSNs.
Figure 16.3 Cumulative distribution of network throughput for direct MI transmission based WUSNs with 10 nodes deployed in a 0.01 km area.
Figure 16.4 Cumulative distribution function of network throughput for MI waveguide based WUSNs with 10 nodes deployed in a 0.01 km area.
Figure 16.5 Cumulative distribution functions of achievable data rates at the sink node.
Chapter 17: Agricultural Applications of Underground Wireless Sensor Systems: A Technical Review
Figure 17.1 Sensor node architecture.
Figure 17.2 A typical terrestrial wireless sensor network
Figure 17.3 A typical wireless underground sensor network
Figure 17.4 Proposed classification of energy conservation approaches [43].
Figure 17.5 Classification of WSN power sources.
Figure 17.6 Proposed classification of fault tolerant techniques for WSNs [48].
Figure 17.7 Network topologies: (a) star; (b) tree; (c) mesh.
Figure 17.8 Different classifications of WSN architectures.
Figure 17.9 Classification of WSN-based applications in the agricultural domain.
Figure 17.10 WSN-based system overview [61].
Figure 17.11 WSN-based irrigation system [61].
Figure 17.12 Matufa's network topology [7].
Figure 17.13 The architecture of WSN-based irrigation system [7].
Figure 17.14 Crop-spraying support system architecture [13].
Figure 17.15 Structure of WSN-based greenhouse control system [26].
Chapter 18: Structural Health Monitoring with WSNs
Figure 18.1 A typical WSN system.
Figure 18.2 Four typical monitoring application areas of WSNs.
Figure 18.3 Statistical results of scholarly literature retrieval queries for IoT, WSNs and SHMs.
Figure 18.4 Three groups of smart sensors.
Figure 18.5 Wireless communication standards, energy versus data rate.
Figure 18.6 Typical use of the Internet for remote monitoring and other SHM services.
Figure 18.7 A block diagram of the wireless PZT SHM system.
Figure 18.8 ‘Concerto Bridge’ in Brunswick equipped with wireless AE sensors (left) and with wireless strain sensors (right).
Figure 18.9 System architecture of an optical fibre sensor WSN platform.
Figure 18.10 LF RFID corrosion monitoring system.
Figure 18.11 Static and transient analysis of corrosion progress for uncoated and coated samples.
Figure 18.12 UHF RFID corrosion monitoring system (left); resonance frequency shift with corrosion thickness/stage change (right).
Figure 18.13 Basic networking topologies.
Chapter 19: Error Manifestations in Industrial WSN Communications and Guidelines for Countermeasures
Figure 19.1 A highly absorbing industrial environment: a paper roll warehouse at Hyltebruk paper mill, Sweden.
Figure 19.2 The floor plan of a paper mill in Borlänge, Sweden.
Figure 19.3 The overlap of IEEE 802.15.4-2006 and IEEE 802.11 spectra at 2.4 GHz [12].
Figure 19.4 The misleading behaviour of quality metrics on a link exposed to WLAN interference: (a) RSSI; (b) LQI [16].
Figure 19.5 The overlap of RSSI/LQI probability density functions derived from correct and corrupted packets on a link exposed to MFA in a paper mill: (a) RSSI; (b) LQI [16].
Figure 19.6 (a) The compact error footprint of WLAN, (b) The sparse error footprint of MFA [20]. The dots denote the positions of corrupt bits.
Figure 19.7 The CDF of bit- and symbol-error burst lengths from a 14-day measurement campaign at three industrial environments: (a) MFA-inflicted errors; (b) WLAN-inflicted errors [20].
Figure 19.8 The performance of bit- and symbol-interleaved and non-interleaved RS(15,7) code on error traces: (a) MFA; (b) WLAN [20].
Figure 19.9 The CDF of chip error rate on WLAN-interfered links in an industrial workshop [25].
Figure 19.10 Comparison of CLAP, conventional DLL FEC (C-FEC) and a state-of the-art approach (LEAD) in terms of PSR.
Figure 19.11 The contributions of different CLAP components, relative to the fully fledged solution [25].
Figure 19.12 The number of chip errors per chip sequence inflicted in a packet corrupted by WLAN interference: (a) before chip matrix de-interleaving; (b) after chip matrix de-interleaving.
Figure 19.13 The boost in packet correctability induced by PREED on links exposed to strong WLAN interference [27].
Figure 19.14 The mean number of packets lost before communication is re-established on a link under: (a) MFA; (b) WLAN interference [31].
Chapter 20: A Medium-access Approach to Wireless Technologies for Reliable Communication in Aircraft
Figure 20.1 Application fault-tree analysis: heat sensor application failure probability.
Figure 20.2 Reliability diagram of an average flight. Dashed lines mark the individual framework layers.
Figure 20.3 Superframe structure of WISA.
Figure 20.4 Superframe structure of ECMA.
Figure 20.5 Reservation-based access cycle in 802.11e.
Figure 20.6 Superframe structure in IEEE 802.15.4.
Figure 20.7 Time-slot structure in WirelessHART.
Figure 20.8 Frame structure of LTE [23].
Figure 20.9 WSAN/FA cycle time vs payload.
Figure 20.10 Cycle time vs number of users in the channel in ECMA.
Figure 20.11 HCCA latency vs the number of users in the channel.
Figure 20.12 IEEE 802.15.4 GTS cycle time against SO parameter.
Figure 20.13 WirelessHART cycle time against network density with the number of frequencies available.
Figure 20.14 LTE cycle time with varying number of users.
Figure 20.15 Cycle times and packet sizes of wireless technologies.
Chapter 21: Applications of Wireless Sensor Systems for Monitoring of Offshore Windfarms
Figure 21.1 WSN deployed on a windfarm. Fixed row tower nodes (RTNs) (large circles) are attached to the towers. RTN node [123] represents the address of fixed node 3 for tower 2 in row 1. The CHs are represented by haxagons. The small circles represent scattered-nodes [35].
Figure 21.2 Flowchart for threshold selection and quantization.
Figure 21.3 Quantization of sea-surface temperatures. Top, original data; middle, FTS quantization, bottom, MM quantization.
Figure 21.4 Repeating and non-repeating level combinations.
Figure 21.5 Block diagram for fault detection using FIS [37].
Figure 21.6 Round with first dead node: (a) 1000 × 1000 m; (b) 4000 × 6000 m.
Figure 21.7 Total number of cluster-heads alive: (a) 1000 × 1000 m; (b) 4000 × 6000 m.
Figure 21.8 Energy efficiency: (a) 1000 × 1000 m; (b) 4000 × 6000 m.
Figure 21.9 Round in which all the nodes become dead.
List of Tables
Chapter 3: Optimizing Lifetime and Power Consumption for Sensing Applications in Extreme Environments
Table 3.1 Symbols for the description of each observation process
Table 3.2 Values of fixed parameters for all plots
Table 3.3 Default parameter values for all plots
Chapter 5: Rare-events Sensing and Event-powered Wireless Sensor Networks
Table 5.1 Advanticsys CM5000 current draw by state
Table 5.2 Duration of coverage maintenance
Table 5.3 OSDI/PGA of noTable earthquakes
Chapter 7: Contact Plan Design for Predictable Disruption-tolerant Space Sensor Networks
Table 7.1 Case study time interval and orbital parameters
Table 7.2 TACP MILP model parameters
Chapter 8: Infrared Wireless Sensor Network Development for the Ariane Launcher
Table 8.1 Dynamic power estimation of the digital circuits
Chapter 9: Multichannel Wireless Sensor Networks for Structural Health Monitoring
Table 9.1 Impact of energy-efficient techniques on sources of energy waste
Table 9.2 Minimum number of slots needed
Table 9.3 The schedule obtained with MODESA for a sink with three radio interfaces and a network with three channels
Table 9.4 Number of slots needed by MODESA for a sink with a single interface
Table 9.5 Number of slots needed by TMCP and MODESA and optimal values
Chapter 11: Navigation and Remote Sensing using Near-space Satellite Platforms
Table 11.1 Typical LEO satellite pass times for different angles above horizon
Table 11.2 Relative advantages of satellites, NSPs and airplanes
Table 11.3 Required number of NSPs in different geometrical configurations
Chapter 14: Coordinates Determination of Submerged Sensors with a Single Beacon Using the Cayley–Menger Determinant
Table 14.1 Properties of radio and acoustic signals
Table 14.2 Coordinates of the sensors with known measurements
Table 14.3 Coordinates of the sensors with respect to beacon
Table 14.4 Coordinates of the sensors with known and computed values
Chapter 17: Agricultural Applications of Underground Wireless Sensor Systems: A Technical Review
Table 17.1 Comparison of different wireless communication technologies
Table 17.2 Comparison of some commercial sensor platforms
Table 17.3 Available sensor nodes for agricultural applications
Table 17.4 Main features of WSN-based system for different agricultural applications
Chapter 18: Structural Health Monitoring with WSNs
Table 18.1 Comparison of above cases for new overlay technologies
Chapter 19: Error Manifestations in Industrial WSN Communications and Guidelines for Countermeasures
Table 19.1 Frequencies of interference generated by typical industrial machinery
Table 19.2 The redefined IEEE 802.15.4-2006 symbol-to-chip mapping [25]
Table 19.3 The number of inferable bytes in WirelessHART packet types
Chapter 20: A Medium-access Approach to Wireless Technologies for Reliable Communication in Aircraft
Table 20.1 Comparison of protocols
Table 20.2 Top-down reliability comparison
Chapter 21: Applications of Wireless Sensor Systems for Monitoring of Offshore Windfarms
Table 21.1 Threshold values
Table 21.2 Fuzzy rule-base for four levels
Table 21.3 Simulation parameters
Wireless Sensor Systems for Extreme Environments
Space, Underwater, Underground, and Industrial
Edited by
Habib F. Rashvand
Advanced Communication Systems University of Warwick UK
Ali Abedi
Department of Electrical and Computer Engineering University of Maine Orono USA
This edition first published 2017
© 2017 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Habib F. Rashvand and Ali Abedi to be identified as the authors of this editorial material in this work has been asserted in accordance with law.
Registered Offices
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office
The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom. If professional advice or other expert assistance is required, the services of a competent professional should be sought.
Library of Congress Cataloging-in-Publication Data
Names: Rashvand, Habib F., editor. | Abedi, Ali, editor.
Title: Wireless sensor systems for extreme environments : space, underwater, underground, and industrial / [edited by] Habib F. Rashvand, Ali Abedi.
Description: Hoboken, NJ : John Wiley & Sons, 2017. | Includes bibliographical references and index.
Identifiers: LCCN 2017005391 (print) | LCCN 2017010421 (ebook) | ISBN 9781119126461 (cloth) | ISBN 9781119126478 (Adobe PDF) | ISBN 9781119126485 (ePub)
Subjects: LCSH: Wireless sensor networks. | Extreme environments.
Classification: LCC TK7872.D48 W587 2017 (print) | LCC TK7872.D48 (ebook) | DDC 004.6-dc23
LC record available at https://lccn.loc.gov/2017005391
A catalogue record for this book is available from the British Library.
Cover design by Wiley
Cover Image: © alex-mit/Gettyimages
Habib dedicates this work to his family, especially Madeleine, Laurence and the late and dearly missed Joan Elmes. Ali dedicates this work to his wife Shahrzad for her unconditional love and support.
List of Contributors
Ali Abedi
Department of Electrical and Computer Engineering
University of Maine
Orono
USA
James Agajo
Federal University of Technology Minna
Ihiagwa
Nigeria
Deepshikha Agarwal
Amity University
Lucknow
Uttar Pradesh
India
Ian F. Akyildiz
Broadband Wireless Networking Lab
Georgia Institute of Technology
Atlanta
USA
Gholamreza Alirezaei
Institute for Theoretical Information Technology
RWTH Aachen University
Aachen
Germany
Filip Barac
Business Unit Network Products
Ericsson AB
Sweden
Gerard Chalhoub
Clermont University
Aubière
France
Xuewu Dai
Northumbria University
Newcastle upon Tyne
UK
Rana Diab
Clermont University
Aubière
France
Lloyd Emokpae
U.S. Naval Research Laboratory
Code 7160 – Acoustics Division
USA
Eriza Fazli
Zodiac Inflight Innovations
Weßling
Germany
Jorge M. Finochietto
Digital Communications Research Laboratory Electronic Department
Universidad Nacional de Córdoba
Córdoba – CONICET
Argentina
Juan A. Fraire
Digital Communications Research Laboratory Electronic Department
Universidad Nacional de Córdoba
Córdoba – CONICET
Argentina
Shang Gao
Nanjing Universit of Aeronautical and Astronautics
China
Wolfgang H. Gerstacker
Institute for Digital Communications
Friedrich-Alexander-Universität Erlangen-Nürnberg
Germany
Mikael Gidlund
Department of Information Systems and Technology
Mid Sweden University
Sweden
Murat Gürsu
Chair of Communication Networks
Technical University of Munich
Germany
David Harrison
School of Engineering and Computer Science
Victoria University of Wellington
New Zealand
Pan Hu
College of Automation Engineering
Nanjing University of Aeronautics and Astronautics
China
Dingde Jiang
College of Information Science and Engineering
Northeastern University
Shenyang
China
Kübra Kalkan
Boğaziçi University
Istanbul
Turkey
Wolfgang Kellerer
Chair of Communication Networks
Technical University of Munich
Germany
Hendra Kesuma
Institute of Electrodynamics and Microelectronics (ITEM)
University of Bremen
Germany
Nand Kishor
Motilal Nehru National Institute of Technology
Lucknow
Uttar Pradesh
India
Steven Kisseleff
Institute for Digital Communications
Friedrich-Alexander-Universität Erlangen-Nürnberg
Germany
Lonnie Labonte
Department of Electronics and Computer Engineering
University of Maine
USA
Albert Levi
Sabancı Üniversitesi
Istanbul
Turkey
Erwan Livolant
Inria Paris
France
Pablo Madoery
Digital Communications Research Laboratory Electronic Department
Universidad Nacional de Córdoba
Córdoba – CONICET
Argentina
Ali Mahani
Department of Electrical Engineering
Shahid Bahonar University of Kerman
Kerman Province
Iran
Rudolf Mathar
Institute for Theoretical Information Technology
RWTH Aachen University
Aachen
Germany
Pascale Minet
Inria Paris
France
Michel Misson
Clermont University
Aubière
France
Vallipuram Muthukkumarasamy
Griffith University
Gold Coast
Australia
Kewen Pan
School of Electrical and Electronic Engineering
University of Manchester
UK
Steffen Paul
Institute of Electrodynamics and Microelectronics (ITEM)
University of Bremen
Germany
Jean-Francois Perelgritz
Airbus Group Innovations
Suresnes
France
Anisur Rahman
East West University
Dhaka
Bangladesh
Habib F. Rashvand
Advanced Communication Systems
University of Warwick
UK
Badr Rmili
CNES Launcher Directorate
Paris
France
Winston K.G. Seah
School of Engineering and Computer Science
Victoria University of Wellington
New Zealand
Johannes Sebald
Airbus Safran Launchers (former EADS Astrium) GmbH
Bremen
Germany
Praveen Shankar
Department of Mechanical and Aerospace Engineering
California State University
California, Long Beach
USA
Saeideh Sheikhpour
Department of Electrical Engineering
Shahid Bahonar University of Kerman
Kerman Province
Iran
Emiliano Sisinni
Department of Information Engineering
University of Brescia
Italy
Ridha Soua
Inria Paris
France
Ali Imam Sunny
School of Electrical and Electronic Engineering
Newcastle University
Newcastle Upon Tyne
UK
Omid Taghizadeh
Institute for Theoretical Information Technology
RWTH Aachen University
Aachen
Germany
Chaoqing Tang
School of Electrical and Electronic Engineering
Newcastle University
Newcastle Upon Tyne
UK
Gui Yun Tian
School of Electrical and Electronic Engineering
Newcastle University
Newcastle upon Tyne
UK
Mikhail Vilgelm
Chair of Communication Networks
Technical University of Munich
Germany
Bang Wang
School of Electronic
Information and Communications
Huazhong University of Science & Technology (HUST)
Wuhan
China
Haitao Wang
College of Automation Engineering
Nanjing University of Aeronautics and Astronautics
China
Wen-Qin Wang
School of Communication and Information Engineering
University of Electronic Science and Technology of China
Chengdu
China
Sherali Zeadally
University of Kentucky
Lexington
US
Tingting Zhang
Department of Information Systems and Technology
Mid Sweden University
Sweden
Jiwen Zhu
Northumbria University
Newcastle upon Tyne
UK
Preface
Just before the turn of the new millennium, the advent of short- and medium-range wireless communication technologies brought rapid developments in the field of lightweight sensors and actuators, creating a fast-moving business area in which new types of smart wireless sensors were brought to market. These smart wireless sensors benefit greatly from the use of infrastructural features of traditional telecommunications and data systems, enabling clustered interworking. New features include autonomous and adaptive coverage. Two common terminologies are ‘wireless sensor networks’ (WSNs) for applications of large to very large numbers of sensing nodes and ‘wireless sensor systems’ (WSS) for smaller numbers.
With the recent surge in commercialization of wireless technology and the availability of advanced coding, signal processing, and information technology, which enable reliable wireless connectivity at high data rates, most industries are reconsidering the need for wires. The saving on weight and cost of wires, cables, fixtures and connectors is an obvious benefit of wireless technology, but extra savings are also associated with design, testing, and modification of sensing systems and these may not always be obvious. Flexibility and scalability of wireless sensing systems are additional benefits.
Space and other extreme environments –underwater, underground – and unconventional industrial environments may significantly benefit from wireless sensing, but due to their challenging environmental conditions and requirements for high reliability, they have not yet fully taken advantage of these technologies. Our recent series of workshops entitled ‘Fly-By-Wireless’ and the parallel workshops called ‘WiSense4Space’ have come together under the IEEE's umbrella to create an IEEE International Conference on Wireless for Space and Extreme Environments. The first of these workshops was in 2013 in Baltimore, USA. This was a technical forum for scientists and engineers from space agencies, industry and academia to share knowledge in the area of wireless for space and extreme environments, and paved the way towards an eventual widespread elimination of wires in challenging environments. Underwater acoustics, optical wireless, communication and sensing inside mines, and of course the most challenging of all wireless environments – space – are among the many topics that have been discussed at these conferences. Discussions continue to date.
This book was motivated by recent advances in the area, and covers a wide variety of topics, from power allocation to battery-free communication, underwater links, and even wireless feedback control. We hope that engineers and scientists who read it will take these ideas and methods to the next level and use them in their future designs. The impact of wireless technology in extreme environments is not just about efficiency and savings due to the elimination of wires. The most important impact is enabling new applications and ways of collecting data from locations that were previously inaccessible. Networked wireless sensors can create a shared data environment that can be used to monitor complex systems, looking for anomalies and acting to prevent disasters. Data collection from structures and machines helps designers develop a better understanding of their designs, determine performance in real-world settings and avoids them having to use simulations. The transition from schedule-based maintenance to condition-based maintenance is another significant advantage of wireless sensors in harsh environments.
We would like to acknowledge the consistent support from our contributing authors, without whom this project would not have been possible. Colleagues from industry, the space agencies and academic institutions around the world – from America, to Europe and to Asia – all worked tirelessly for over two years to prepare the chapters of this book. Their contributions and dedication to this project is highly appreciated.
We would like to thank the John Wiley publishing team for their guidance and efforts from the early stages of draft proposals, to reviewing the final production stage, so this editorial volume could be presented at the required level: a high-quality book for the academic and industrial research and development community.
We sincerely hope that these efforts have created a useful book for students to learn from, to inspire engineers and scientists to use these concepts in their designs, and for academic instructors to teach these emerging concepts.
We believe that the advanced technologies of ‘fly by wireless’, ‘drive by wireless’, and ‘live by wireless’, apart from their convenience, effective use ofsmart sensors should enable our industries in space, Earth, cities, and homes to be more environmentally friendly.
The editorsHabib F. Rashvand and Ali Abedi
Part IWireless Sensor Systems for Extreme Environments-Generic Solutions
Chapter 1Wireless Sensor Systems for Extreme Environments
Habib F. Rashvand1 and Ali Abedi2
1Advanced Communication Systems, University of Warwick, UK
2Department of Electrical and Computer Engineering, University of Maine, Orono, USA
Taking a new step, uttering a new word, is what people fear most
Fyodor Dostoyevsky
1.1 Introduction
The last 40 years of economic and political unrest has wrought a series of drastic changes throughout the world. Many technological trends have come to a halt as new developments have taken over, surprising the experts. Amongst the successful ones are smart sensing, flourishing as a result of promises of a higher quality of life and worries about the deterioration of the climate.
Although there have been many projects throughout the world and many successful civil and industrial applications, we are still awaiting to see a real paradigm shift. As increasing resources have expanded and increased research activity, too many research reports have somehow failed to demonstrate the eye-catching industrial applications required to justify the resources being expended. To this end, we have to judge on a global scale the performance of sensors in the last 20 years; we have looked at earlier surveys [1] and analysed the economic effectiveness of the projects described. One of the main conclusions is that too many young researchers try to make their work publishable rather than practical and useful for real applications that to help improve the quality of life. As well as the few useful research activities – such as energy conservation, optimized performance, cross layering, efficient sampling, and data management – we see many trivial patterns of common networking manipulation: routing, scheduling, node replacement, mobility, and coverage under oversimplified working conditions, where simple computer simulations can generate huge volumes of inaccurate data; they are simply creating a new black hole for consuming computer resources.
Following our series of conferences on wireless technologies for space and extreme environments (WiSEE) and the associated sensor workshops we have decided that we need to direct research towards the environments that need sensors most: space and other harsh, industrial or unconventional environments.
Following Edison's problem-solving attitude when demonstrating the use of electricity to create the light to brighten our nights, we need to encourage our youth to have strong belief and true dedication. They need to enjoy creativity and achieving their objectives so that they can engineer a better quality of life. They should be solving problems, breaking the old boundaries, opening new windows of opportunity and creating new paradigms. Applying new technologies, such as wireless and ever-improving smart sensors and actuators, gives us many possibilities for creating new and much smarter technological systems and services.
To be successfully deployed, a new technology must meet four basic measures: trust, objectivity, security, and sustainability. Here, objectivity is the demand for a product or service, which in our case means overcoming unconventional working conditions, to that the working product or a system enables new services, whether in the vacuum of space, in the oceans, underground or in places with very high, very low, and highly variable temperature, humidity, winds and pressure.
The rest of this chapter is devoted to two main summary sections. Section 1.2 describes our earlier work on wireless sensor systems (WSSs) for space and other extreme environments, while Section 1.3 provides an extended summary of the remaining twenty-one chapters of the book.
1.2 Wireless Sensor Systems for Space and other Extreme Environments
This section summarises our earlier review of our work on WSSs for space and extreme environments [1]. This was based on our WSS workshop at WiSEE 2013. Our main message is this section is to analyse how to break away from conventional wireless sensor networks (WSNs) by adopting an agile heterogeneous unconventional wireless sensing (UWS) deployment system.
1.2.1 Definitions
A comparative analysis is better than a simple definition of the terms, which often can vary upon application scenarios and its working environment.
Wireless sensor networks (WSNs) are normally complex networks of large numbers of interconnected sensor nodes and clusters. A wireless sensor system (WSS), however, is a smaller-scale system of data-oriented interconnected sensing devices for extracting well-defined sensing information. The sensor nodes in WSSs are expected to be less constrained and more flexible, and therefore more adaptive and autonomous. In WSSs, use of terms such as wireless sensor and actuator networks, wireless smart intelligent sensing, wirelessly connected distributed smart sensing, and unmanaged aerial vehicle sensor networks makes sense. However, wireless underground sensor networks, underwater wireless sensor networks, wireless body-sensor mesh networks and industrial wireless sensor networks are normally more complex, and are therefore more applicable to WSNs by definition.
As heterogeneous sensing services require UWS solutions, one way to compare WSSs and WSNs is to look objectively at the purpose for which they are designed. WSS-based solutions for self-managed heterogeneous sensing services are more dynamic and practical if kept small. This is due to our basic service principles:
conventional WSNs, normally deployed for homogenous sensing services using generic smart sensors
unconventional WSSs, designed for dynamic, heterogeneous, UWS services using specific sensors.
UWS solutions therefore require to be kept simple and they therefore suit smaller and less complex WSSs.
1.2.2 Networking in Space and Extreme Environments
In many WSNs, the simplicity of the data collection can allow deployment of sensors on multi-service networks, in which densely distributed sensors and actuators are used for a wide range of applications. In space and extreme environments (SEEs) smart networking is needed to make this process more efficient, and so it can benefit from the low-cost, low-power operation of networks. For example, a multi-timescale adaptation routing protocol can use multi-timescale estimation to minimize variation of packet transmission times by calculating the mean and variance. Another example is the deployment of distributed radar sensor networks (RSNs), grouped together in an intelligent cluster network in an ad hoc fashion. These can then provide spatial resilience for target detection and tracking. Such RSNs may be used for tactical combat systems deployed on airborne, surface, and subsurface unmanned vehicles in order to protect critical infrastructure.
1.2.3 Node Synchronization in SEEs
Management aspects of WSNs for time synchronization and cooperative collaboration of the nodes is important in SEEs. Techniques such as the sliding clock synchronization protocol is used for time synchronization under extreme temperatures. The key aspect of this protocol is a central node that periodically sends time synchronization signals. Then, the node measures the time between two consecutive signals as well as the locally measured time, from which it can determine and rectify any possible errors.
Another good example is creation of an ultra-reliable WSN that will never stop monitoring, even in extreme conditions, and does not require maintenance. Such a system can detect a failing sensor node through a dynamic routing protocol, enabling other nodes to take over the function being carried out by the dead node.
1.2.4 Spectrum Sharing in SEEs
In space, the demand for spectrum is huge, particularly where the safety of personnel and the reliability of control systems are heavily dependent on wireless sensors such as:
structural health
impact detection and location
leak detection and localization.
Robust and reliable dynamic spectrum-sharing schemes are needed. In order to make use of spectrum-sharing in space, we need to make modify systems used in terrestrial networks, in which, for example, errors in spectrum sensing are unavoidable but which often lack incentives for primary users to allow network access to secondary users.
1.2.5 Energy Aspects in SEE
Medium access control (MAC) plays a crucial role in providing energy-efficient and low-delay communications for WSNs. Sensing systems designed for operation in space or underwater face additional challenges, notably long and potentially variable propagation delays, which severely inhibit the throughput capability and delay performance of conventional MAC schemes. Outages due to energy shortages and adverse propagation conditions also pose significant problems. We now examine similar challenges associated with reliable and efficient multiple access in SEEs, focusing on underwater sensing systems.
The use of energy-harvesting technology has important implications for medium access, since uncertainty surrounding the future availability of energy makes it difficult to arrange reliable duty-cycles, schedules or back-off times in the traditional way. The challenges associated with long propagation delays are well understood for satellite systems. Demand assignment multiple access is commonly employed as a means of achieving high channel utilization, since capacity can be allocated to nodes in response to time-varying requirements.
1.3 Chapter Abstracts
The chapters for this book come from two sources:
expansions of previously published journal or conference papers, where the authors' work has already been peer reviewed
original reviews to expand the scope of the book, at the choice of senior and experienced academic or industrial experts.
1.3.1 Abstract of Chapter 2
This chapter, entitled ‘Feedback Control Challenges with Wireless Networks in Extreme Environments’, presents a new perspective on feedback control systems that operate in a wireless fashion. Motivated by the high cost of installing a wired control system in aerospace vehicles and even automobiles and the added weight and fuel requirements that comes with it, this chapter aims at redesigning control systems by eliminating wires from sensors to the controller and eventually to actuators. Replacing wires with wireless links in a control system may be modeled in different ways. This chapter describes a delay and noise model with parameters coming from the wireless system. The performance of the control system is then studied with added delay and noise in the loop to address the feasibility of wireless control.
A case study is presented in which a launch vehicle is instrumented with several accelerometer sensors to model the vibration modes. This information will be useful for fine-tuning the trajectory of a rocket as its structure bends at high speeds. The system dynamics and controller are modeled using first- and second-order differential equations with a parameter used to determine rise time, settling time, and overshoot of the closed-loop response of the system.
A fixed delay is then added to the system and presented in rational form using the Pade approximation. The effect of the delay on the stability of a first-order system is then studied. This result is further extended to multi-sensor inputs with different delays. The effect of the delay on the transient response of a second-order system is studied too.
External disturbances affecting a wireless link are modeled using an additive white Gaussian noise model, which will slightly alter the parameters of the system's differential equation. Rise time and overshoot changes versus noise are plotted and analysed.
Although there is still a long way to go before such systems are implemented in critical applications, this chapter lays the groundwork for modeling, analysing and studying such systems and presents a framework for designing a wireless controller for sensor and actuator networks in SEEs.
1.3.2 Abstract of Chapter 3
This chapter, entitled ‘Optimizing Lifetime and Power Consumption for Sensing Applications in Extreme Environments’, considers power optimization in a general sensor network; that is, without specifying any particular application. An optimization problem is defined with a view to extending the network's lifetime by using the minimum power possible. The proposed problem is shown to be convex, therefore having a global solution that can be obtained by applying traditional numerical methods and convex optimization theory.
