Chen Wang, Ruonan Zhang, Haotong Cao, Junhao Song, W. Zhang
{"title":"Joint optimization for latency minimization in UAV-assisted MEC networks","authors":"Chen Wang, Ruonan Zhang, Haotong Cao, Junhao Song, W. Zhang","doi":"10.1145/3555661.3560858","DOIUrl":null,"url":null,"abstract":"Combining unmanned aerial vehicles (UAVs) with multi-access edge computing (MEC) networks has been deemed as a potential approach for delay-sensitive applications. In this paper, we propose a UAV-assisted MEC network architecture and jointly optimize the UAVs' position, task offloading, bandwidth allocation, and computing resource allocation to minimize the time consumption of each terminal devices cluster. To solve this problem, we design a joint optimization algorithm based on the particle swarm optimization (PSO) and bisection searching (BSS) approach. The results of the simulation reveal that the devised algorithm can significantly reduce time consumption and guarantee the fairness of the whole network.","PeriodicalId":151188,"journal":{"name":"Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555661.3560858","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Combining unmanned aerial vehicles (UAVs) with multi-access edge computing (MEC) networks has been deemed as a potential approach for delay-sensitive applications. In this paper, we propose a UAV-assisted MEC network architecture and jointly optimize the UAVs' position, task offloading, bandwidth allocation, and computing resource allocation to minimize the time consumption of each terminal devices cluster. To solve this problem, we design a joint optimization algorithm based on the particle swarm optimization (PSO) and bisection searching (BSS) approach. The results of the simulation reveal that the devised algorithm can significantly reduce time consumption and guarantee the fairness of the whole network.