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Efficient Deployment of Multiple UAVs for IoT Communication in Dynamic Environment

查看全文 作　　者：Haibo [1]Dai;Haiyang [2]Zhang;Chunguo [3]Li;Baoyun [4]Wang 高影响力作者 机构地区：[1]School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;[2]Engineering Systems and Design Pillar,Singapore University of Technology and Design,487372,Singapore;[3]School of Information Science and Engineering,Southeast University,Nanjing 210096,China;[4]College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China高影响力机构 出　　处：《China Communications》索引2020年第17卷第1期,共15页高影响力期刊 基　　金：supported in part by the Natural Science Foundation of China under Grants 61801243, 61671144, and 61971238;by the China Postdoctoral Science Foundation under Grant 2019M651914;by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 18KJB510026;by the Foundation of Nanjing University of Posts and Telecommunications under Grant NY218124 摘　　要：The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm. 关 键 词：IOT UAV communication stochastic game Nash equilibrium distributed learning
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