UAV Swarm Cooperation by Yao, Haipeng; Mai, Tianle;

Long description:

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This book provides a comprehensive examination of Unmanned Aerial Vehicles (UAV) swarm collaboration from a networking perspective. It systematically analyzes key components such as network topology construction, efficient routing algorithms and resource management strategies.

The second chapter addresses adaptive clustering and dynamic network planning, enabling UAV swarms to adjust their topologies and maintain robust structures in fluctuating environments. The third chapter introduces intelligent routing algorithms designed to optimize network resilience and performance metrics, including lifetime, packet delivery rate and throughput. Chapter four investigates resource scheduling challenges proposing virtualization-based strategies for the optimal allocation of computational and communication resources. Chapters five through seven discuss the opportunities and challenges posed by emerging network technologies. It includes encompassing semantic communication for enhanced data transfer efficiency, the application of distributed learning techniques (e.g., federated and reinforcement learning) for intelligent UAV swarm networks and deterministic networking approaches that ensure low-latency, reliable control in UAV precision-critical operations.

Overall, this book serves as an authoritative reference that integrates state-of-the-art technologies and algorithmic designs to address the multifaceted challenges and opportunities in UAV swarm networks. It’s designed for advanced-level students, professors, engineers, and researchers learning and working in the fields of the loT Networks. Industry managers, partitioners and government research agencies working in this field will also find this book a useful reference.

Table of Contents:

1. Introduction of Unmanned Aerial Vehicles Swarm Networks.- 2. Adaptive UAV Swarm Networks Topology Organization.- 2.1. Introduction of Adaptive UAV Swarm Clustering and Networking.- 2.2. Fission Spectral Clustering Strategy for UAV Swarm Networks.- 2.3. Graph Attentional Based Agglomerative Cluster for UAV Swarm Networks.- 2.4. Adaptive State-aware UAV Swarm Networking Organization.- 3. Intelligent UAV Swarm Networks Routing.- 3.1. Introduction of Intelligent UAV Swarm Networks Routing.- 3.2. Evolutionary Game based Dynamic Routing in UAV Swarm Networks.- 3.3. Cooperative Learning based Dynamic Routing UAV Swarm Networks.- 3.4. Multi-agent Reinforcement Learning aided UAV Swarm Routing.- 4. On-demand UAV Network Resource Scheduling.- 4.1. Introduction of UAV Network Resource Scheduling.- 4.2. Graph Transformer Aided Resource Virtualization Embedding.- 4.3. Privacy-Driven Security-Aware Task Scheduling in Swarm Network.- 4.4. Stackelberg Game-Based Offloading Strategy for Swarm Networks.- 5. Semantic Communication Empowered UAV Swarm Networks.- 5.1. Introduction of Semantic Communication.- 5.2. Semantic Communication Aided UAV Swarm Networks.- 5.3. Incentive Semantic-aware UAV Swarm Networks Coordination.- 6. Federated Learning empowered UAV Swarm Networks.- 6.1. Introduction of Federated Learning.- 6.2. Clustered Federated Learning in Heterogeneous UAV Swarms Networks.- 6.3. Automatic Auction for Federated Learning in UAV Swarms Networks.- 7. Deterministic Networking Empowered UAV Swarm.- 7.1. Introduction of Deterministic Networking.- 7.2. Learning-Based Deterministic Traffic Scheduling for UAV Swarm.- 7.3. Hierarchical Scheduling Mechanism for Deterministic Traffic for UAV Swarm.- 8. Conclusions.