Submission Deadline: 30 December 2024 View: 257 Submit to Special Issue
Mobile edge computing (MEC) enabled unmanned aerial vehicle (UAV) communication has emerged as a promising technique for wireless devices to realize low latency and high reliability communication and computation services in a more flexible and cost-effective manner. However, the small-scale UAVs are incompetence in handling more complex missions, such as earth monitoring, precision agriculture and large-scale military deployment. As a result, edge computing enabled UAV swarm intelligence has attracted great attention from academia and industry in recent years. It is envisioned that edge computing enabled UAV swarm intelligence can provide strong support for us to embrace the forthcoming era of “Internet of Drones (IoD)” and gain wide popularity in supporting future human activities. In order to facilitate the implementation of edge computing enabled UAV swarm intelligence, several preliminary research work have been carried out including resource allocation of edge computing enabled UAV swarm intelligence and dynamic spectrum management of edge computing enabled UAV swarm intelligence.
Although these emerging issues have drawn considerable attention and have been studied recently, there are still many open theoretical and practical problems to be addressed. Specifically, in order to ensure low execution latency and high energy efficiency of edge computing enabled UAV swarm intelligence, how to reduce UAV-to-ground and UAV-to-UAV interference, need to be further investigated. In addition, note that the severe intra-swarm wireless interference, the uncertainty of wireless channel and data processing latency will inevitably cause response delay of UAV, which impairs the stability of the UAV swarm. Therefore, more research efforts are needed to investigate the effective robust automatic networking technologies for keeping stability of large-scale UAV swarm. Furthermore, computing task sharing has a huge risk of privacy leakage, which prompts the computational security in the edge computing enabled UAV swarm intelligence to be an attentional issue.
The aim of this special issue is to provide a new comprehensive overview on UAV swarm and create more ideas on edge computing enabled UAV swarm intelligence, which will bring together researchers from academia, industry and governmental agencies to promote the research and development needed to address the major challenges that pertain to this cutting-edge research topic.
Potential topics include but are not limited to:
• Performance analysis for edge computing enabled IoD
• Implementation issues in edge computing enabled IoD
• Resource allocation strategies for multi-UAV communication
• Multi-antenna techniques for edge computing enabled IoD
• Multi-access techniques for UAV swarm intelligence
• Energy-efficient cooperative sensing techniques for UAV swarm intelligence
• Physical layer security for edge computing enabled UAV swarm intelligence
• Enhanced 3D spectrum sensing for UAV swarm intelligence
• Machine learning and deep learning for edge computing enabled UAV swarm intelligence
• Federated learning for edge computing enabled UAV swarm intelligence
• Mission oriented multi-UAV automatic networking
• Energy-efficient UAV path planning/computation offloading
• Spectrum-efficient techniques for edge computing enabled IoD
• Intelligent reflection surface assisted resource allocation/edge computing
• UAV assisted task oriented intelligent semantic communication
• Other emerging techniques for UAV swarm-enabled edge computing
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