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Smart city article details

Title	Energy-Aware Edge Computing Resource Scheduling Method
ID_Doc	23421
Authors	Zhu G.; Li Q.; Li W.; Lv D.; Guo Y.
Year	2023
Published	Proceedings of SPIE - The International Society for Optical Engineering, 12941
DOI	http://dx.doi.org/10.1117/12.3011803
Abstract	Currently, various energy sources such as electricity, heat, gas, and water in cities are relatively independently supplied, making it difficult to meet the demand for integrated energy utilization in new smart cities. The urban energy supply structure urgently needs to evolve from single transmission to gradual integration and coupling of multiple flows, which highlights the pressing need to enhance capabilities such as accurate perception of energy status data, trustworthy fusion and sharing, and precise energy efficiency services. These capabilities will greatly contribute to the improvement of overall urban energy efficiency and economic structure optimization. This article primarily focuses on the scheduling of computational resources between edge servers and terminal devices. Edge computing reduces data transmission latency and enhances computational agility during the computation offloading process. However, the computational capacity of edge servers is also limited, and in certain specific computation offloading scenarios (such as ultra-dense networks), interference may arise, leading to unexpected transmission delays. Therefore, it is not advisable to offload all tasks for execution on edge servers; some tasks should be handled by the terminal devices (SMD). Despite consuming more energy through local execution, this approach eliminates the need to consider data transmission time, thus significantly boosting task responsiveness. This article aims to design a task offloading scheme that is aware of terminal energy consumption and involves scheduling computational resources between edge servers and terminal devices. © 2023 SPIE. All rights reserved.
Author Keywords	Algorithm; Communication Cost; Computational Resource; Computing Power; Edge Computing; SMD

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