Abstract
The increasing growth of maritime activities leads to the challenges for processing the maritime data. However, the resources-limited maritime devices cannot meet the requirements of transmission delay and energy consumption. In this paper, we investigate the resource allocation for computation offloading in maritime communication networks via game theory to improve the offloading efficiency and reduce the energy consumption of maritime devices. Specifically, we propose an optimization problem that jointly optimizes the offloading data, the computation resource allocation of unmanned surface vehicle (USV) and the allocation of acoustic channels, with the objective of minimizing the total energy consumption of underwater wireless sensor (UWS). Despite the non-convexity of the joint optimization problem, we propose a layered structure and decompose it into a top-problem for optimizing the data offloading, a middle-problem for optimizing the computation resource allocation of USV, a bottom problem for optimizing the channel allocation. We conduct simulations to validate the effectiveness and efficiency of the proposed algorithms.
This work was supported in part by National Natural Science Foundation of China under Grants 62122069, 62072490, and 62071431, in part by FDCT-MOST Joint Project under Grant 0066/2019/AMJ, in part by Science and Technology Development Fund of Macau SAR under Grants 0060/2019/A1 and 0162/2019/A3, in part by the Intergovernmental International Cooperation in Science and Technology Innovation Program under Grant 2019YFE0111600, in part by FDCT SKL-IOTSC(UM)-2021-2023, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515011287 and GDST 2020B1212030003, and in part by Research Grant of University of Macau under Grants MYRG2020-00107-IOTSC and MYRG2018-00237-FST.
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Dai, M., Luo, Z., Wang, T., Wu, Y., Qian, L., Lin, B. (2022). Optimal Resource Allocation for Computation Offloading in Maritime Communication Networks: An Energy-Efficient Design via Matching Game. In: Fang, F., Shu, F. (eds) Game Theory for Networks. GameNets 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-23141-4_14
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