Abstract
The research on the Internet of Things (IoT) and edge computing, especially Unmanned Aerial Vehicles assisted Mobile Edge Computing (UAV assisted MEC) attracted more and more interests of researchers. Nowadays, Electric Vehicles (EV) was introduced into the UAV assisted MEC system to improve system suitability and robustness. In this paper, an UAV and EV assisted MEC system was proposed and a paralled processing was involved with the goal of the edge side energy efficiency maximization, by jointly optimizing the communication scheduling, computing frequency and the trajectory of the UAV. The problem was formulated as a Mixed Integer Non-Liner Programming (MINLP), which was hard to solve. Therefore, the MINLP was divided into three sub-problems by applying the Block Coordinate Descent (BCD) method and solved by using Exhaustive Method (EM) and Successive Convex Optimization (SCO). In addition, a heuristic algorithm was put forward to get the optimization solution. The simulation results showed that our strategy has better performance compared with other benchmarks.
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Acknowledgement
This work was jointly supported by Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ30736), Changsha Municipal Natural Science Foundation (Grant No. kq2014112), and the Outstanding Youth Project of Hunan Province Education Department (Grant No. 18B162).
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Tang, Q., Li, L., Wang, J., Kim, Gj., Tang, B. (2022). Energy Efficiency Maximization for UAV and Electric Vehicle Assisted Mobile Edge Computing on the Edge Side. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_37
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DOI: https://doi.org/10.1007/978-3-031-06791-4_37
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