{"title":"UAV-Assisted MEC Architecture for Collaborative Task Offloading in Urban IoT Environment","authors":"Subhrajit Barick;Chetna Singhal","doi":"10.1109/TNSM.2025.3535094","DOIUrl":null,"url":null,"abstract":"Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use uncrewed aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider’s (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV topology optimization. We develop a Three-sided Matching with Size and Cyclic preference (TMSC) based task offloading algorithm to find stable association between IoTs, UAVs, and ESs to achieve system objective. We also propose a K-means based iterative algorithm to decide the minimum number of UAVs and their positions to provide offloading services to maximum IoTs in the system. Finally, we demonstrate the efficacy of the proposed task offloading scheme over benchmark schemes through simulation-based evaluation. The proposed scheme outperforms by 19%, 12%, and 25% on average in terms of percentage of served IoTs, average user satisfaction, and SP profit, respectively, with 25% lesser UAVs, making it an effective solution to support IoT task requirements in urban environments using UAV-assisted MEC architecture.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"732-743"},"PeriodicalIF":4.7000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network and Service Management","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10855598/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use uncrewed aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider’s (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV topology optimization. We develop a Three-sided Matching with Size and Cyclic preference (TMSC) based task offloading algorithm to find stable association between IoTs, UAVs, and ESs to achieve system objective. We also propose a K-means based iterative algorithm to decide the minimum number of UAVs and their positions to provide offloading services to maximum IoTs in the system. Finally, we demonstrate the efficacy of the proposed task offloading scheme over benchmark schemes through simulation-based evaluation. The proposed scheme outperforms by 19%, 12%, and 25% on average in terms of percentage of served IoTs, average user satisfaction, and SP profit, respectively, with 25% lesser UAVs, making it an effective solution to support IoT task requirements in urban environments using UAV-assisted MEC architecture.
期刊介绍:
IEEE Transactions on Network and Service Management will publish (online only) peerreviewed archival quality papers that advance the state-of-the-art and practical applications of network and service management. Theoretical research contributions (presenting new concepts and techniques) and applied contributions (reporting on experiences and experiments with actual systems) will be encouraged. These transactions will focus on the key technical issues related to: Management Models, Architectures and Frameworks; Service Provisioning, Reliability and Quality Assurance; Management Functions; Enabling Technologies; Information and Communication Models; Policies; Applications and Case Studies; Emerging Technologies and Standards.