Joint Optimization of Multi-UAV Deployment and Computational Offloading for Multi-Point Streaming Tasks

Jianlei Xu, Yijun Guo, Jianjun Hao, Zijing Chen

Abstract: Multiple unmanned aerial vehicles (UAVs) computation offloading plays a major role in the sixth generation (6G) mobile networks for resolving conflicts between computation-intensive or time-sensitive tasks and sensor nodes (SNs) with limited capabilities. Emerging artificial intelligence businesses such as multimodal data confusion and multi-video streams processing bring new challenges to UAV-aided communication and computation networks due to their multi-point and streaming characteristics. This paper focuses on a multi-UAV aided multi-point streaming tasks scenario where the SNs generate subtasks comprising various workflows randomly in a service period. We model the sequential relationship between multi-point tasks and time constraints for different workflows and aim to minimize the average energy consumption of all SNs while satisfying different time constraints by jointly optimizing the UAV-SN association policy and UAV deployment locations. To address the non-convexity problem, we decompose the problem into two subproblems, which we then solve alternately with the Markov approximation algorithm and convex optimization until convergence. Numerical results show that the proposed algorithm can effectively lower the average energy consumption of all SNs by a maximum of 35.6% compared to other schemes while maintaining a successful service ratio of 93%.