Journal of Grid Computing, Год журнала: 2025, Номер 23(2)
Опубликована: Май 8, 2025
Язык: Английский
ACM Computing Surveys, Год журнала: 2023, Номер 55(13s), С. 1 - 37
Опубликована: Янв. 11, 2023
Wireless Powered Mobile Edge Computing (WPMEC) is an integration of (MEC) and Power Transfer (WPT) technologies, to both improve computing capabilities mobile devices energy compensation for their limited battery capabilities. Generally, transmitters, devices, edge servers form a WPMEC system that realizes closed loop sending collecting as well offloading receiving task data. Due constraints time-varying network environments, time-coupled levels, the half-duplex character joint design computation resource allocation solutions in systems has become extremely challenging, great number studies have been devoted it recent years. In this article, we first introduce basic model system. Then, present key issues techniques related WPMEC. addition, summarize solve critical networks. Finally, discuss some research challenges open issues.
Язык: Английский
Процитировано
90
IEEE Communications Surveys & Tutorials, Год журнала: 2023, Номер 25(4), С. 2088 - 2132
Опубликована: Янв. 1, 2023
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements, and machine learning capabilities, which leads a growing need for highly efficient intelligent algorithms. classic optimization-based algorithms usually require precise mathematical model of data links suffer poor performance with computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models theoretical tools), (ML) stands out as promising viable methodology many complex large-scale problems 6G, due its superior performance, efficiency, scalability, generalizability. In this paper, we systematically review most representative "learning optimize" techniques diverse domains networks identifying inherent feature underlying problem investigating specifically designed ML frameworks perspective optimization. particular, will cover algorithm unrolling, branch-and-bound, graph neural network structured optimization, deep reinforcement stochastic end-to-end semantic well federated distributed capable addressing challenging arising variety crucial Through in-depth discussion, shed light excellent ML-based respect classical methods, provide insightful guidance develop advanced networks. Neural design, tools different implementation issues, challenges future research directions also discussed support practical use
Язык: Английский
Процитировано
55
IEEE Access, Год журнала: 2023, Номер 11, С. 25329 - 25350
Опубликована: Янв. 1, 2023
An inflection point in the computing industry is occurring with implementation of Internet Things and 5G communications, which has pushed centralized cloud toward edge resulting a paradigm shift computing. The purpose to provide computing, network control, storage accommodate computationally intense latency-critical applications at resource-limited endpoints. Edge allows devices offload their overflowing tasks servers. This procedure may completely exploit server's computational capabilities efficiently execute operations. However, transferring all an server leads long processing delays surprisingly high energy consumption for numerous tasks. Aside from this, unused powerful centers lead resource waste. Thus, hiring collaborative scheduling approach based on task properties, optimization targets, system status servers, centers, critical successful operation paper briefly summarizes architecture information processing. Meanwhile, scenarios are examined. Resource techniques then discussed compared four collaboration modes. As part our survey, we present thorough overview various offloading schemes proposed by researchers Additionally, according literature surveyed, looked fairness load balancing indicators scheduling. Finally, issues, challenges, future directions have discussed.
Язык: Английский
Процитировано
49
Nano Energy, Год журнала: 2024, Номер 130, С. 110186 - 110186
Опубликована: Авг. 27, 2024
Язык: Английский
Процитировано
18
IEEE Access, Год журнала: 2024, Номер 12, С. 118007 - 118059
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
13
Cluster Computing, Год журнала: 2025, Номер 28(3)
Опубликована: Янв. 21, 2025
Язык: Английский
Процитировано
2
Multimedia Tools and Applications, Год журнала: 2024, Номер 83(26), С. 67953 - 67996
Опубликована: Янв. 29, 2024
Язык: Английский
Процитировано
8
Computer Science Review, Год журнала: 2024, Номер 54, С. 100680 - 100680
Опубликована: Сен. 9, 2024
Язык: Английский
Процитировано
8
Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 125 - 135
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
7
Sensors, Год журнала: 2024, Номер 24(24), С. 7918 - 7918
Опубликована: Дек. 11, 2024
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection cloud servers in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on detect anomalies reducing the need continuous transfer cloud. Meanwhile, a Long Short-Term Memory (LSTM) analyzes time-series analysis, enhancing scheduling operational efficiency. The framework’s dynamic workload management algorithm optimizes task distribution between resources, balancing usage, consumption. Experimental results show that approach achieves 35% reduction 28% decrease 60% usage compared cloud-only solutions. framework offers scalable, efficient solution real-time maintenance, making it highly applicable resource-constrained, data-intensive environments.
Язык: Английский
Процитировано
7