Aerial drones, in general known as Unmanned Aerial Vehicles (UAVs), holds a longstanding history of being utilized within mobile networks as network processors; however, a shift has been observed where they are now being utilized as mobile servers within the framework of Mobile Edge Computing (MEC). This evolution is primarily attributed to their inherent flexibility, portability, robust line-of-sight communication capabilities, and cost-effectiveness, which allows for adaptable usage scenarios, thereby contributing to an increase in their utilization within both research and commercial settings. The essential characteristics of aerial drones have made them increasingly popular across a wide spectrum of civilian services, such as transportation, industrial monitoring, agriculture, forest fire management, and wireless services. Within the scope of this project, the focus lies on exploring MEC networks utilizing Unmanned Aerial Vehicles, where these UAVs undertake computational tasks provided by mobile terminal users (TUs). In order to guarantee the Quality-of-Service (QoS) for each TU, the UAV makes real-time modifications to its flight path by taking into consideration the positions of the mobile TUs, with the ultimate goal of improving the overall performance of the network and enhancing the user experience.
Deep Reinforcement Learning-Driven Path Optimization in Multi-UAV Edge Computing Systems
Communic.Comp.Inf.Science
International Conference on Advanced Network Technologies and Intelligent Computing ; 2024 ; Varanasi, India December 18, 2024 - December 20, 2024
2025-03-08
21 pages
Article/Chapter (Book)
Electronic Resource
English
Joint optimization of UAV aided covert edge computing via a deep reinforcement learning framework
Elsevier | 2024
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