AUTHOR=Lai Qijie , Xie Rongchang , Yang Zhifei , Wu Guibin , Hong Zechao , Yang Chao TITLE=Efficient multiple unmanned aerial vehicle-assisted data collection strategy in power infrastructure construction JOURNAL=Frontiers in Communications and Networks VOLUME=5 YEAR=2024 URL=https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2024.1390909 DOI=10.3389/frcmn.2024.1390909 ISSN=2673-530X ABSTRACT=

Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAV-assisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms.