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E-grāmata: Wireless Mesh Networks for IoT and Smart Cities: Technologies and applications

Edited by (University of Parma, Internet of Things (IoT) Laboratory, Italy), Edited by (University of Parma, Internet of Things (IoT) Laboratory, Italy)
  • Formāts: PDF+DRM
  • Sērija : Telecommunications
  • Izdošanas datums: 21-Jun-2022
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839532832
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Wireless Mesh Networks for IoT and Smart Cities: Technologies and applicationsPalielināt
  • Formāts: PDF+DRM
  • Sērija : Telecommunications
  • Izdošanas datums: 21-Jun-2022
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839532832
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Wireless Mesh Networks (WMNs) are wireless communication networks organized in a mesh topology with radio capabilities. These networks can self-form and self-heal and are not restricted to a specific technology or communication protocol. They provide flexible yet reliable connectivity that cellular networks cannot deliver. Thanks to technological advances in machine learning, software defined radio, UAV/UGV, big data, IoTs, and smart cities, wireless mesh networks have found much renewed interest for communication network applications.

This edited book covers state of the art research innovations and future directions in this field. WMNs offer attractive communication solutions in difficult environments such as emergency situations, battlefield surveillance, field operations, disaster recovery, tunnels, oil rigs, high-speed mobile-video applications on board transport, VoIP, and self-organizing internet access for communities. The main topics covered include BLL-based mesh networks, body sensor networks, seamless IoT mobile sensing through Wi-Fi mesh networking, software defined radio for wireless mesh networks, UAV-to-ground multi-hop communication using backpressure and FlashLinQ-based algorithms, unmanned aerial vehicle relay networks, multimedia content delivery in wireless mesh networking, adaptive fuzzy agents in big data and multi-sensor environments and AI-aided resource sharing for WMNs.

This is a useful reference for ICT networking engineers, researchers, scientists, engineers, advanced students and lecturers in both academia and industry working on wireless communications and WMNs. It is also relevant to developers, designers and manufacturers of WMNs and WSNs; and scientists and engineers working on applications of WNNs and wireless sensor networks.



Covering new developments and state-of-the-art research in Wireless Mesh Networks (WMNs), this edited book is a useful reference for ICT networking engineers, researchers, scientists, engineers, advanced students and lecturers in both academia and industry working on Wireless Communications and WMNs.

About the Editors xiii
1 Wireless mesh network emulation
1(20)
Ramon dos Reis Fontes
Augusto Jose Vendncio Neto
Christian Esteve Rothenberg
1.1 Introduction
1(1)
1.2 Mininet-WiFi: a primer
2(3)
1.3 Overview of wireless mesh technologies
5(5)
1.3.1 IBSS (ad hoc)
5(1)
1.3.2 Wireless distribution system
6(1)
1.3.3 WiFi direct
7(1)
1.3.4 IEEE 802.15.4 (6L0WPAN)
8(1)
1.3.5 IEEE 802.11p
9(1)
1.4 Routing protocols for WMN
10(5)
1.4.1 IEEE 802.11s
10(1)
1.4.2 OLSRd
11(1)
1.4.3 Babeld
12(1)
1.4.4 B.A.T.M.A.N.
12(1)
1.4.5 Summary
13(2)
1.5 Experimental use cases
15(2)
1.5.1 A realistic vehicular experimentation
15(1)
1.5.2 Unmanned aerial vehicles
16(1)
1.6 Conclusion
17(4)
References
17(4)
2 A sink-oriented routing protocol for blue light link-based mesh network
21(12)
Luca Davoli
Massimo Moreni
Gianluigi Ferrari
2.1 Introduction
21(1)
2.2 Related works
22(2)
2.3 Sink-oriented routing protocol
24(1)
2.3.1 Receive message (RMS) packet
24(1)
2.4 Topology construction (downlink)
25(1)
2.5 Data collection -- request (downlink)
26(1)
2.6 Data collection -- response (uplink)
27(1)
2.7 Use cases
27(1)
2.7.1 Network topology reconstruction
27(1)
2.7.2 Sensing of BLE devices in the neighborhood
28(1)
2.8 Conclusions
28(5)
Acknowledgement
28(1)
References
28(5)
3 Body sensor networks---recent advances and challenges
33(34)
Shama Siddiqui
Anwar Ahmed Khan
Indrakshi Dey
3.1 Introduction
33(2)
3.2 Applications of BSN
35(4)
3.2.1 Medical applications
35(3)
3.2.2 Nonmedical applications
38(1)
3.3 Body sensor networks---overview and components
39(4)
3.3.1 Overview
39(1)
3.3.2 Components
40(3)
3.4 BSN architecture
43(9)
3.4.1 Intra-BSN communication
43(5)
3.4.2 Inter-BSN communication
48(1)
3.4.3 Beyond-BSN communication
49(2)
3.4.4 BSN network topologies
51(1)
3.5 BSN network layers
52(7)
3.5.1 Physical layer
53(1)
3.5.2 Medium access control layer
54(2)
3.5.3 Network layer
56(1)
3.5.4 Application layer
57(2)
3.6 Security threats and solutions for BSN
59(2)
3.6.1 Active security threats for BSN
59(1)
3.6.2 Passive security threats for BSN
60(1)
3.6.3 Security solutions
60(1)
3.7 Opportunities and open research directions
61(3)
3.8 Conclusion
64(3)
References
64(3)
4 Seamless IoT mobile sensing through Wi-Fi mesh networking
67(14)
Antonio Cilfone
Luca Davoli
Laura Belli
Gianluigi Ferrari
4.1 Introduction
67(1)
4.2 Background
68(3)
4.2.1 IEEE 802.1 Is basics
68(1)
4.2.2 IEEE 802.11s routing algorithm
69(1)
4.2.3 B.A.T.M.A.N.
69(2)
4.3 Mesh network implementation
71(5)
4.3.1 Proposed mesh backbone network
72(4)
4.4 Conclusions and application scenarios
76(5)
Acknowledgments
78(1)
References
78(3)
5 Software-defined radio for wireless mesh networks
81(24)
Rafik Zitouni
Laurent George
Stefan Ataman
5.1 Introduction
81(2)
5.2 Challenges for the wireless mesh networks
83(2)
5.2.1 Cross-layer design
83(1)
5.2.2 Experiment of WMN communications
84(1)
5.2.3 Rigid implementation of standards
84(1)
5.2.4 Scarcity of spectrum
84(1)
5.3 Software-defined radio (SDR)
85(4)
5.3.1 Architecture
85(4)
5.4 Performances analysis of SDR platform
89(5)
5.4.1 Analysis of USRP boards driven by GNU Radio
91(3)
5.5 SDR for IEEE 802.15.4e
94(2)
5.5.1 Dynamic spectrum access
95(1)
5.6 SDR for IEEE 802.11p
96(3)
5.6.1 Non-orthogonal multiple access
97(2)
5.7 Conclusion
99(6)
References
100(5)
6 Backpressure and FlashLinQ-based algorithms for multi-hop flying ad-hoc networks
105(16)
Benjamin Okolo
Chiara Buratti
Roberto Verdone
6.1 Introduction
105(2)
6.2 System model
107(2)
6.2.1 Reference scenario
107(2)
6.2.2 Channel model
109(1)
6.3 The proposed algorithms
109(5)
6.3.1 Trajectory-based joint backpressure and FlashLinQ
110(3)
6.3.2 Predictive trajectory-based joint backpressure and FlashLinQ
113(1)
6.4 The benchmark solution
114(1)
6.5 Numerical results and discussions
115(3)
6.5.1 Simulator setup
115(1)
6.5.2 Comparing protocols
115(3)
6.6 Conclusion
118(3)
References
119(2)
7 Unmanned aerial vehicle relay networks
121(20)
Evsen Yanmaz
7.1 Introduction
121(2)
7.2 System model
123(2)
7.2.1 Assumptions
123(1)
7.2.2 Pre-defined mission paths
124(1)
7.3 Path planning for UAV relay networks
125(4)
7.3.1 Relay positioning and assignment algorithm
125(4)
7.3.2 Illustration of different PMST methods
129(1)
7.4 Results and discussions
129(8)
7.4.1 Percentage connected time
131(1)
7.4.2 Required number of relays
131(4)
7.4.3 Average relay node velocity
135(2)
7.5 Conclusions
137(4)
References
138(3)
8 Multimedia content delivery in wireless mesh networking
141(32)
Ting Bi
Shengyang Chen
8.1 Introduction
141(1)
8.2 Multimedia content delivery and quality evaluation
142(2)
8.2.1 Overview
142(1)
8.2.2 Quality of service requirements
142(2)
8.3 Video content delivery quality measurement
144(3)
8.3.1 Subjective and objective quality assessment
144(3)
8.4 Energy consumption issues during content delivery
147(2)
8.5 Protocols, schemes, and algorithms
149(1)
8.5.1 Transport layer protocols
149(1)
8.6 MAC-layer schemes
150(4)
8.6.1 QoS-related wireless mesh MAC-layer schemes
150(3)
8.6.2 Energy-related wireless mesh MAC-layer schemes
153(1)
8.7 Routing protocols and algorithms
154(5)
8.7.1 Routing protocols
154(3)
8.7.2 Routing algorithms
157(1)
8.7.3 Routing mechanisms in wireless mesh networks
158(1)
8.8 Multimedia content delivery services
159(3)
8.8.1 Overview
159(1)
8.8.2 Streaming service
160(2)
8.9 Research-related works
162(2)
8.10 Industrial solutions and products
164(2)
8.11 Challenging multimedia content
166(7)
8.11.1 3D video
166(1)
8.11.2 VR, AR, 360-degree videos, and mulsemedia content
166(2)
References
168(5)
9 Toward intelligent extraction of relevant information by adaptive fuzzy agents in big data and multi-sensor environments
173(16)
Zakarya Elaggoune
Ramdane Maamri
Allel Hadjali
Imane Boussebough
9.1 Introduction
173(4)
9.2 Our previous work
177(5)
9.2.1 The fuzzy agent approach
177(3)
9.2.2 The limits of the previous work
180(2)
9.3 Toward a learning fuzzy agent approach for relevant data extraction in big data and multi-sensor environments
182(2)
9.3.1 An overview of the novel approach
182(2)
9.4 Conclusion
184(5)
References
186(3)
10 Artificial intelligence-aided resource sharing for wireless mesh networks
189(36)
Indrakshi Dey
Georgios Ropokis
Nicola Marchetti
10.1 Introduction
189(1)
10.2 ML-assisted resource sharing
190(18)
10.2.1 ML for resource sharing in WMN
192(1)
10.2.2 Supervised learning
193(6)
10.2.3 Unsupervised learning
199(4)
10.2.4 Reinforcement learning
203(5)
10.3 DL-assisted resource sharing
208(4)
10.3.1 Deep learning
208(2)
10.3.2 Deep RL
210(1)
10.3.3 Graph neural network
211(1)
10.4 Distributed intelligence-assisted resource sharing
212(10)
10.4.1 Federated learning
213(3)
10.4.2 Collective awareness
216(1)
10.4.3 Game-theoretic approach
217(5)
10.5 Outlook
222(3)
References
223(2)
11 Boosting machine learning mechanisms in wireless mesh networks through quantum computing
225(22)
Francesco Vista
Vittoria Musa
Giuseppe Piro
Luigi Alfredo Grieco
Gennaro Boggia
11.1 Introduction
225(2)
11.2 The role of ML in WMNs
227(4)
11.2.1 Supervised learning
227(1)
11.2.2 Unsupervised learning
228(1)
11.2.3 Reinforcement learning
229(1)
11.2.4 Deep learning
229(1)
11.2.5 Deep RL
230(1)
11.2.6 Open issues in the application of ML for WMNs
230(1)
11.3 Quantum computing: background and QML
231(4)
11.3.1 Superposition principle
231(1)
11.3.2 Quantum measurement
231(1)
11.3.3 No-cloning theorem
232(1)
11.3.4 Entanglement
232(1)
11.3.5 Teleportation
233(1)
11.3.6 Quantum ML
234(1)
11.4 Introduction of QML in WMNs: design principles and research challenges
235(8)
11.4.1 Centralized architecture
235(4)
11.4.2 Distributed architecture
239(4)
11.5 Conclusions
243(4)
References
243(4)
12 Game theoretical-based task allocation in malicious cognitive Internet of Things
247(18)
Marco Martalo
Virginia Pilloni
Talha Faizur Rahman
Luigi Atzori
12.1 Introduction
247(2)
12.2 Related work
249(1)
12.3 Reference scenario
250(1)
12.4 The task allocation strategy
251(7)
12.4.1 Spectrum sensing in malicious cognitive IoT
251(5)
12.4.2 Cluster node bidding
256(2)
12.5 Simulation results
258(4)
12.6 Concluding remarks
262(3)
References
262(3)
13 Conclusions and future perspectives
265(2)
Luca Davoli
Gianluigi Ferrari
Index 267
Luca Davoli is a fixed-term assistant professor at the University of Parma, Italy. Since January 2014, he has been a member of the Internet of Things (IoT) Laboratory (https://iotlab.unipr.it) at the Department of Engineering and Architecture of the University of Parma, Italy. His main research interests include Internet of Things, software defined networking, big stream and peer-to-peer networks. He is an IEEE and a GTTI member. He received his PhD in information technologies from the Department of Information Engineering of the same university with a thesis entitled "Architecture and Technologies for the Internet of Things".



Gianluigi Ferrari is an associate professor of telecommunications at the University of Parma, Italy. Since September 2006 he has been the Coordinator of the Internet of Things (IoT) Laboratory (http://iotlab.unipr.it/) and, since 2016, the co-founder, president and CEO of things2i s.r.l. (http://www.things2i.com/), a spin-off company of the University of Parma dedicated to IoT and smart systems. His research activities revolve around signal processing, communication/networking, and IoT. He has published extensively in these areas and coordinated several technical projects, including EU-funded competitive projects. He is a senior member of the IEEE. He received his PhD in information technologies from the University of Parma, Italy.
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