Wireless Mesh Networks for IoT and Smart Cities: Technologies and applications

Contents
About the Editors
1 Wireless mesh network emulation
1.1 Introduction
1.2 Mininet-WiFi: a primer
1.3 Overview of wireless mesh technologies
1.3.1 IBSS (ad hoc)
1.3.2 Wireless distribution system
1.3.2.1 The 4-address
1.3.3 WiFi direct
1.3.4 IEEE 802.15.4 (6LoWPAN)
1.3.5 IEEE 802.11p
1.4 Routing protocols for WMN
1.4.1 IEEE 802.11s
1.4.2 OLSRd
1.4.2.1 OLSRd2
1.4.3 Babeld
1.4.4 B.A.T.M.A.N.
1.4.4.1 Batmand
1.4.4.2 Batman-adv
1.4.5 Summary
1.5 Experimental use cases
1.5.1 A realistic vehicular experimentation
1.5.2 Unmanned aerial vehicles
1.6 Conclusion
References
2 A sink-oriented routing protocol for blue light link-based mesh network
2.1 Introduction
2.2 Related works
2.3 Sink-oriented routing protocol
2.3.1 Receive message (RMS) packet
2.4 Topology construction (downlink)
2.5 Data collection – request (downlink)
2.6 Data collection – response (uplink)
2.7 Use cases
2.7.1 Network topology reconstruction
2.7.2 Sensing of BLE devices in the neighborhood
2.8 Conclusions
Acknowledgements
References
3 Body sensor networks—recent advances and challenges
3.1 Introduction
3.2 Applications of BSN
3.2.1 Medical applications
3.2.2 Nonmedical applications
3.3 Body sensor networks—overview and components
3.3.1 Overview
3.3.2 Components
3.3.2.1 Sensors
3.3.2.2 Actuators
3.3.2.3 Types of nodes
3.3.2.4 Antennas
3.4 BSN architecture
3.4.1 Intra-BSN communication
3.4.1.1 System model
3.4.1.2 EM-based intra-BSN
Propagation model
Noise model
Achievable transmission rate
3.4.2 Inter-BSN communication
3.4.2.1 System model
3.4.1.3 Molecular communication-based intra-BSN
Propagation model
Noise model
Achievable transmission rate
3.4.2.2 Propagation model
3.4.3 Beyond-BSN communication
3.4.3.1 System model
3.4.3.2 Propagation model
3.4.3.3 Noise model
3.4.4 BSN network topologies
3.5 BSN network layers
3.5.1 Physical layer
3.5.2 Medium access control layer
3.5.3 Network layer
3.5.4 Application layer
3.6 Security threats and solutions for BSN
3.6.1 Active security threats for BSN
3.6.2 Passive security threats for BSN
3.6.3 Security solutions
3.7 Opportunities and open research directions
3.8 Conclusion
References
4 Seamless IoT mobile sensing through Wi-Fi mesh networking
4.1 Introduction
4.2 Background
4.2.1 IEEE 802.11s basics
4.2.2 IEEE 802.11s routing algorithm
4.2.3 B.A.T.M.A.N.
4.3 Mesh network implementation
4.3.1 Proposed mesh backbone network
4.4 Conclusions and application scenarios
Acknowledgments
References
5 Software-defined radio for wireless mesh networks
5.1 Introduction
5.2 Challenges for the wireless mesh networks
5.2.1 Cross-layer design
5.2.2 Experiment of WMN communications
5.2.3 Rigid implementation of standards
5.2.4 Scarcity of spectrum
5.3 Software-defined radio (SDR)
5.3.1 Architecture
5.3.1.1 Software communication architecture (SCA)
5.3.1.2 Embedded software-defined radio
Reconfigurable hardware-based architecture
General purpose processor (GPP)-based architecture
5.4 Performances analysis of SDR platform
5.4.1 Analysis of USRP boards driven by GNU Radio
5.4.1.1 Measurement approach
5.4.1.2 Measurement results
5.5 SDR for IEEE 802.15.4e
5.5.1 Dynamic spectrum access
5.6 SDR for IEEE 802.11p
5.6.1 Non-orthogonal multiple access
5.6.1.1 Design of NOMA/SIC transceivers
5.6.1.2 Benefits of NOMA/SIC
5.7 Conclusion
Acknowledgements
References
6 Backpressure and FlashLinQ-based algorithms for multi-hop flying ad-hoc networks
6.1 Introduction
6.2 System model
6.2.1 Reference scenario
6.2.2 Channel model
6.3 The proposed algorithms
6.3.1 Trajectory-based joint backpressure and FlashLinQ
6.3.2 Predictive trajectory-based joint backpressure and FlashLinQ
6.4 The benchmark solution
6.5 Numerical results and discussions
6.5.1 Simulator setup
6.5.2 Comparing protocols
6.6 Conclusion
References
7 Unmanned aerial vehicle relay networks
7.1 Introduction
7.2 System model
7.2.1 Assumptions
7.2.2 Pre-defined mission paths
7.3 Path planning for UAV relay networks
7.3.1 Relay positioning and assignment algorithm
7.3.1.1 Pre-planned relay paths
7.3.1.2 Online path planning due to an event
7.3.2 Illustration of different PMST methods
7.4 Results and discussions
7.4.1 Percentage connected time
7.4.2 Required number of relays
7.4.3 Average relay node velocity
7.5 Conclusions
References
8 Multimedia content delivery in wireless mesh networking
8.1 Introduction
8.2 Multimedia content delivery and quality evaluation
8.2.1 Overview
8.2.2 Quality of service requirements
8.2.2.1 Delay
8.2.2.2 Jitter
8.2.2.3 BER
8.2.2.4 Packet loss
8.2.2.5 Throughput
8.3 Video content delivery quality measurement
8.3.1 Subjective and objective quality assessment
8.3.1.1 Subjective methods
8.3.1.2 Objective methods
8.4 Energy consumption issues during content delivery
8.5 Protocols, schemes, and algorithms
8.5.1 Transport layer protocols
8.5.1.1 Datagram Congestion Control Protocol (DCCP)
8.5.1.2 Stream Control Transmission Protocol (SCTP)
8.5.1.3 Multipath Transmission Control Protocol (MPTCP)
8.6 MAC-layer schemes
8.6.1 QoS-related wireless mesh MAC-layer schemes
8.6.2 Energy-related wireless mesh MAC-layer schemes
8.7 Routing protocols and algorithms
8.7.1 Routing protocols
8.7.2 Routing algorithms
8.7.3 Routing mechanisms in wireless mesh networks
8.8 Multimedia content delivery services
8.8.1 Overview
8.8.2 Streaming service
8.8.2.1 HTTP-based adaptive streaming standards
8.8.2.2 Streaming service over WMNs
8.9 Research-related works
8.10 Industrial solutions and products
8.11 Challenging multimedia content
8.11.1 3D video
8.11.2 VR, AR, 360-degree videos, and mulsemedia content
References
9 Toward intelligent extraction of relevant information by adaptive fuzzy agents in big data and multi-sensor environments
9.1 Introduction
9.2 Our previous work
9.2.1 The fuzzy agent approach
9.2.2 The limits of the previous work
9.3 Toward a learning fuzzy agent approach for relevant data extraction in big data and multi-sensor environments
9.3.1 An overview of the novel approach
9.4 Conclusion
References
10 Artificial intelligence-aided resource sharingfor wireless mesh networks
10.1 Introduction
10.2 ML-assisted resource sharing
10.2.1 ML for resource sharing in WMN
10.2.2.1 k-Nearest neighbors (k-NN)
10.2.2.2 Support vector machine
10.2.2.3 Random forestBefore talking about random forest
10.2.2.4 Neural networks
10.2.3 Unsupervised learning
10.2.3.1 K-means clustering
10.2.3.2 Gaussian mixture model
10.2.4 Reinforcement learning
10.2.4.1 Markov decision process
10.2.4.2 Q-Learning
10.2.4.3 Multi-armed bandits
10.3 DL-assisted resource sharing
10.3.1 Deep learning
10.3.2 Deep RL
10.3.3 Graph neural network
10.4 Distributed intelligence-assisted resource sharing
10.4.1 Federated learning
10.4.2 Collective awareness
10.4.3 Game-theoretic approach
10.4.3.1 Static games with complete information
10.4.3.2 Static games with incomplete information
10.4.3.3 Potential games
10.4.3.4 Nash bargaining games
10.4.3.5 Non-cooperative games with complete information
10.4.3.6 Cooperative games
10.5 Outlook
References
11 Boosting machine learning mechanisms in wireless mesh networks through quantum computing
11.1 Introduction
11.2 The role of ML in WMNs
11.2.1 Supervised learning
11.2.2 Unsupervised learning
11.2.3 Reinforcement learning
11.2.4 Deep learning
11.2.5 Deep RL
11.2.6 Open issues in the application of ML for WMNs
11.3 Quantum computing: background and QML
11.3.1 Superposition principle
11.3.2 Quantum measurement
11.3.3 No-cloning theorem
11.3.4 Entanglement
11.3.5 Teleportation
11.3.6 Quantum ML
11.4 Introduction of QML in WMNs: design principles and research challenges
11.4.1 Centralized architecture
11.4.1.1 The information exchange in the centralized architecture
11.4.1.2 Benefits and research challenges
11.4.2 Distributed architecture
11.4.2.1 The information exchange in the distributed architecture
11.4.2.2 Benefits and research challenges
11.5 Conclusions
References
12 Game theoretical-based task allocation in malicious cognitive Internet of Things
12.1 Introduction
12.2 Related work
12.3 Reference scenario
12.4 The task allocation strategy
12.4.1 Spectrum sensing in malicious cognitive IoT
12.4.2 Cluster node bidding
12.5 Simulation results
12.6 Concluding remarks
References
13 Conclusions and future perspectives
Index