{"title":"优化移动边缘计算的能效:利用延迟感知卸载、集群和无人机放置策略","authors":"","doi":"10.1016/j.aeue.2024.155447","DOIUrl":null,"url":null,"abstract":"<div><p>In the context of next-generation 5G and beyond communication networks, integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) is crucial. Hybrid Non-orthogonal Multiple Access (H-NOMA) has been recognized as a prominent technique for reducing energy consumption during data offloading. However, the literature assumes that all users in the cluster have latency requirements and interference levels such that implementing H-NOMA is optimal, overlooking other scenarios. Furthermore, the position of UAV-hosted MEC is not optimized. To address these constraints, we propose an adaptive offloading method where users can utilize either H-NOMA or OMA for data offloading in designated time slots based on their conditions. We substantiate this proposal through a comparative analysis of energy consumption between H-NOMA and OMA. Additionally, we introduce a novel Maximum Latency Difference Clustering and Power Allocation (MLDC & PA) algorithm for organizing smart terminals (STs) and allocating power. Furthermore, we propose a heuristic-based optimization approach for UAV positioning to minimize offloading energy and enhance network efficiency. Simulation results confirm that the proposed approach has superior energy consumption reduction capabilities compared to state-of-the-art techniques.</p></div>","PeriodicalId":50844,"journal":{"name":"Aeu-International Journal of Electronics and Communications","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing energy efficiency in mobile edge computing: Leveraging latency-aware offloading, clustering, and UAV placement strategies\",\"authors\":\"\",\"doi\":\"10.1016/j.aeue.2024.155447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the context of next-generation 5G and beyond communication networks, integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) is crucial. Hybrid Non-orthogonal Multiple Access (H-NOMA) has been recognized as a prominent technique for reducing energy consumption during data offloading. However, the literature assumes that all users in the cluster have latency requirements and interference levels such that implementing H-NOMA is optimal, overlooking other scenarios. Furthermore, the position of UAV-hosted MEC is not optimized. To address these constraints, we propose an adaptive offloading method where users can utilize either H-NOMA or OMA for data offloading in designated time slots based on their conditions. We substantiate this proposal through a comparative analysis of energy consumption between H-NOMA and OMA. Additionally, we introduce a novel Maximum Latency Difference Clustering and Power Allocation (MLDC & PA) algorithm for organizing smart terminals (STs) and allocating power. Furthermore, we propose a heuristic-based optimization approach for UAV positioning to minimize offloading energy and enhance network efficiency. Simulation results confirm that the proposed approach has superior energy consumption reduction capabilities compared to state-of-the-art techniques.</p></div>\",\"PeriodicalId\":50844,\"journal\":{\"name\":\"Aeu-International Journal of Electronics and Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aeu-International Journal of Electronics and Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1434841124003339\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aeu-International Journal of Electronics and Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1434841124003339","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Optimizing energy efficiency in mobile edge computing: Leveraging latency-aware offloading, clustering, and UAV placement strategies
In the context of next-generation 5G and beyond communication networks, integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) is crucial. Hybrid Non-orthogonal Multiple Access (H-NOMA) has been recognized as a prominent technique for reducing energy consumption during data offloading. However, the literature assumes that all users in the cluster have latency requirements and interference levels such that implementing H-NOMA is optimal, overlooking other scenarios. Furthermore, the position of UAV-hosted MEC is not optimized. To address these constraints, we propose an adaptive offloading method where users can utilize either H-NOMA or OMA for data offloading in designated time slots based on their conditions. We substantiate this proposal through a comparative analysis of energy consumption between H-NOMA and OMA. Additionally, we introduce a novel Maximum Latency Difference Clustering and Power Allocation (MLDC & PA) algorithm for organizing smart terminals (STs) and allocating power. Furthermore, we propose a heuristic-based optimization approach for UAV positioning to minimize offloading energy and enhance network efficiency. Simulation results confirm that the proposed approach has superior energy consumption reduction capabilities compared to state-of-the-art techniques.
期刊介绍:
AEÜ is an international scientific journal which publishes both original works and invited tutorials. The journal''s scope covers all aspects of theory and design of circuits, systems and devices for electronics, signal processing, and communication, including:
signal and system theory, digital signal processing
network theory and circuit design
information theory, communication theory and techniques, modulation, source and channel coding
switching theory and techniques, communication protocols
optical communications
microwave theory and techniques, radar, sonar
antennas, wave propagation
AEÜ publishes full papers and letters with very short turn around time but a high standard review process. Review cycles are typically finished within twelve weeks by application of modern electronic communication facilities.