Flying Ad Hoc Networks: Cooperative Networking and Resource Allocation (Wireless Networks)

Preface
Contents
Acronyms
1 Introduction of Flying Ad Hoc Networks
	1.1 Basic Classification and Regulation of UAVs
	1.2 Differences Between FANET, VANET, MANET, and AANET
	1.3 Compelling Applications of FANET
	References
2 Communication Channels in FANET
	2.1 UAV Communication Channel Characteristics
		2.1.1 UAV Link Budget
		2.1.2 UAV Channel Fading
		2.1.3 Channel Impulse Response and Metrics
	2.2 UAV Communication Channel Modeling
		2.2.1 Air-to-Ground Channels
			2.2.1.1 A2G Channels in Urban Areas
			2.2.1.2 Low-Altitude Channels in Cellular Networks
			2.2.1.3 A2G Channels in Rural and Over-Water Areas
			2.2.1.4 Evaporation Duct for Over Sea
			2.2.1.5 Aircraft Shadowing in A2G Channels
		2.2.2 Air-to-Air Channels
		2.2.3 UAV-MIMO Channels
			2.2.3.1 UAV-MIMO Channel Modeling
			2.2.3.2 Antenna Diversity
			2.2.3.3 Spatial Multiplexing
	2.3 Challenges and Open Issues
		2.3.1 Antennas for UAV Channel Measurement
		2.3.2 Channels of UAV Applications in IoT and 5G
		2.3.3 Channels in Vertical Industrial Applications
		2.3.4 Channels of UAV FSO Communications
	References
3 Seamless Coverage Strategies of FANET
	3.1 Introduction of Seamless Coverage Problems
		3.1.1 Problem Domain and Challenges
		3.1.2 State of the Art
	3.2 UAV Seamless Coverage Strategy for Dense Urban Areas
		3.2.1 System Model
		3.2.2 Cyclic Recharging and Reshuffling Optimization
			3.2.2.1 UAV Power Model
			3.2.2.2 CRRS Constraint
		3.2.3 Problem Formulation
		3.2.4 Distributed Particle Swarm Optimization Aided Solution
			3.2.4.1 Analysis and Simplification
			3.2.4.2 Distributed-PSO Algorithm Design
			3.2.4.3 Algorithmic Convergence Analysis
			3.2.4.4 Algorithmic Complexity Analysis
		3.2.5 Simulation Results
		3.2.6 Conclusions
	3.3 UAV Seamless Coverage Strategy for QoS-Guaranteed IoT
		3.3.1 System Model
		3.3.2 Problem Formulation
		3.3.3 Block Coordinate Descent Based Joint Optimization
			3.3.3.1 Node Assignment Scheduling
			3.3.3.2 UAV Trajectory Planning
			3.3.3.3 UAV Transmit Power Control
			3.3.3.4 Algorithmic Architecture and Convergence Analysis
		3.3.4 Simulation Results
			3.3.4.1 Resulting Strategies
			3.3.4.2 Energy Efficiency
			3.3.4.3 Optimality Analysis
		3.3.5 Conclusions
	3.4 UAV Seamless Coverage Strategy for Minimum-Delay Placement
		3.4.1 System Model
			3.4.1.1 Physical Layer Model of the UAV-Enabled Network
			3.4.1.2 Queuing Model and System Dynamics
			3.4.1.3 ABS Placement Scheduling
		3.4.2 Problem Formulation
		3.4.3 Markov Decision Process Transformation
			3.4.3.1 Constrained Markov Decision Process
			3.4.3.2 The Lagrangian Approach
		3.4.4 Backward Induction and R-Learning Based Optimization
			3.4.4.1 Solution to the Problem in Case 1
			3.4.4.2 Solution to the Problem in Case 2
			3.4.4.3 Solution to the Problem in Case 3
			3.4.4.4 Analysis of Computational Complexity
		3.4.5 Simulation Results
			3.4.5.1 Impact of the ABS\' Total Energy
			3.4.5.2 Impact of the Asymmetry Wireless Tele-Traffic
			3.4.5.3 Impact of the Wireless Tele-Traffic Rate
			3.4.5.4 Impact of the Ground Devices\' Location
		3.4.6 Conclusions
		3.4.7 The Proof of Theorem 1
	References
4 Cooperative Resource Allocation in FANET
	4.1 Introduction of Cooperative Resource Allocation Problems
		4.1.1 Problem Domain and Challenges
		4.1.2 State of the Art
	4.2 UAV Position Control with Interference
		4.2.1 System Model
		4.2.2 Problem Formulation
			4.2.2.1 Constraints
			4.2.2.2 Uplink Resource Allocation Formulation
		4.2.3 Hovering Altitude and Power Control Solution
			4.2.3.1 Stage 1: Joint Subchannel and Power Control
			4.2.3.2 Lagrangian Dual Decomposition Method
			4.2.3.3 Stage 2: Hovering Altitude Optimization
			4.2.3.4 Joint Hovering Altitude and Power Control
			4.2.3.5 Algorithm Implementation
			4.2.3.6 Supplementary Analysis
		4.2.4 Simulation Results
		4.2.5 Conclusions
	4.3 UAV Trajectory Design for Space–Air–Ground Networks
		4.3.1 System Model
		4.3.2 Problem Formulation
		4.3.3 The Solution for Optimization Problem
			4.3.3.1 Smart Devices Connection Scheduling Optimization
			4.3.3.2 Power Control Optimization
			4.3.3.3 The UAV Trajectory Optimization
			4.3.3.4 Optimization of Joint Smart Device Connection Scheduling, Power Control, and UAV Trajectory Design
			4.3.3.5 Computational Complexity Analysis
		4.3.4 Simulation Results
		4.3.5 Conclusions
	4.4 Multi-UAV-Aided IoT NOMA Uplink Transmission
		4.4.1 System Model
			4.4.1.1 Channel Model
			4.4.1.2 Interference Model
		4.4.2 Problem Formulation
		4.4.3 IoT Nodes Clustering and Subchannel Assignment
		4.4.4 Power Allocation and Flight Height Design
			4.4.4.1 Power Allocation Design of IoT Nodes
			4.4.4.2 Flight Heights Design of UAVs
			4.4.4.3 Joint Power Allocation and Flight Height Optimization
		4.4.5 Simulation Results
		4.4.6 Conclusions
	References
5 Mobile Edge Computing in FANET
	5.1 Introduction of Mobile Edge Computing Problems
		5.1.1 Problem Domain and Challenges
		5.1.2 State of the Art
	5.2 Load-Balance Oriented UAV-Aided Edge Computing
		5.2.1 System Model
			5.2.1.1 Network Model
			5.2.1.2 Communication Model
			5.2.1.3 Computation Model
		5.2.2 Problem Formulation
		5.2.3 Joint UAV Deployment and Task Scheduling
			5.2.3.1 Load Balance for UAVs
			5.2.3.2 GAP Based Node Assignment
			5.2.3.3 Deep Reinforcement Learning Aided Task Scheduling
			5.2.3.4 Differential Evolution Based Multi-UAV Deployment
		5.2.4 Simulation Results
		5.2.5 Conclusions
	5.3 Latency and Reliability Guaranteed UAV-Aided Edge Computing
		5.3.1 System Model
			5.3.1.1 Joint Communications and Computing Optimization
		5.3.2 Problem Formulation
		5.3.3 Hybrid Binary Particle Swarm Optimization
		5.3.4 Simulation Results
		5.3.5 Conclusions
	5.4 Energy-Efficient and Secure UAV-Aided Edge Computing
		5.4.1 System Model
			5.4.1.1 Local-Computing Model
			5.4.1.2 Jamming Model
			5.4.1.3 Secure Offloading Model
			5.4.1.4 Edge Computing Model
		5.4.2 Problem Formulation
			5.4.2.1 Problem 1: Active Eavesdropper
			5.4.2.2 Problem 2: Passive Eavesdropper
		5.4.3 Energy-Efficient Secure UMEC Solution
			5.4.3.1 Case 1: Active Eavesdropper
			5.4.3.2 Case 2–1: Passive Eavesdropper at a Fixed Location
			5.4.3.3 Case 2–2: Passive Eavesdropper at a Random Location
			5.4.3.4 Optimal Offloading Strategy for the Secure UMEC
		5.4.4 Analysis of Offloading and Computation
			5.4.4.1 Zero Offloading
			5.4.4.2 Full Offloading
			5.4.4.3 Partial Offloading
			5.4.4.4 Computational Overload
		5.4.5 Simulation Results
			5.4.5.1 Selection of Offloading Options
			5.4.5.2 Impact of SOP Requirements
			5.4.5.3 Impact of the UAV\'s Altitude and of the Eavesdropper\'s Location
		5.4.6 Conclusions
	5.5 Transmit-Energy and Computation-Delay Optimization
		5.5.1 System Model
			5.5.1.1 The UAV Model
			5.5.1.2 The Channel Model
			5.5.1.3 Cloud Computation Model
			5.5.1.4 Edge Cloud
			5.5.1.5 Remote Cloud
		5.5.2 Energy-Efficient Gateway Selection
			5.5.2.1 The Communication Model Analysis
			5.5.2.2 Required Transmission Time and Energy Consumption
			5.5.2.3 An Energy-Efficient Gateway Selection Scheme
		5.5.3 Task Scheduling and Resource Allocation Scheme
			5.5.3.1 Average Power Consumption and Cloud Execution Delay
			5.5.3.2 Task Scheduling and Resource Allocation Scheme Based on Lyapunov Optimization
			5.5.3.3 A Low-Complexity Iterative Algorithm
		5.5.4 Simulation Results
			5.5.4.1 Performance of Gateway Selection Scheme
			5.5.4.2 Performance of Task Scheduling and Resource Allocation scheme
		5.5.5 Conclusions
	References