Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, ...
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As systems become larger and more complex, real-world problems, such as system operation, require that quasi-optimal solutions with sufficient engineering optimality be obtained in practical time. Meta-heuristics have...
Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features f...
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One of the major challenges in the world is to control the impacts of global warming and greenhouse gas emissions on the environment. To reduce CO2 emission, an environment friendly substitute for traditional fossil f...
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作者：
Wan, XinWong, Man-ChungUniversity of Macau
State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering Faculty of Science and Technology China
Due to the rapid development of power electronics and new energy generation, conventional transformers cannot meet the higher requirements of intelligent grids due to a lack of controllability. While solid-state trans...
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The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data fr...
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ISBN:
(数字)9798350368369
ISBN:
(纸本)9798350368376
The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data from ground-based user equipment (UE). This paper addresses the dual challenges of optimizing both UAV deployment and task offloading within such networks to minimize communication latency and efficiently utilize UAV resources, which are limited by battery life and processing capabilities. We propose a bi-level optimization framework that simultaneously tackles the placement of UAVs and the distribution of computational tasks among them. At the higher level, UAV deployment is optimized to ensure minimal distance to the UEs, thereby reducing latency and energy consumption during data transmission. At the lower level, task offloading is optimized to balance the computational load across the UAV network, considering each UAV's capacity and battery constraints. We demonstrate through extensive simulations the significant improvements in system efficiency, latency, and resilience. This approach not only enhances the performance of UAV-assisted MEC networks but also provides scalable solutions adaptable to various operational scenarios.
Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelm...
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Recently, the emergence of smart cities (SC), where data streams come from various geographically distributed places, has posed new challenges. Cloud Computing provides excellent services for smart cities, such as pow...
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Social Economical Cultural (SECs) costs, increasing haunt of the personal cars caused by insufficient services and decreasing the quality of life in cosmopolitans. Development of Electronic-City (E-City) using Interne...
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