research-article

Resource Orchestration of Cloud-Edge–based Smart Grid Fault Detection

Authors:

Zhi LiuAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 18, Issue 3

Article No.: 46, Pages 1 - 26

https://doi.org/10.1145/3529509

Published: 24 August 2022 Publication History

Abstract

Real-time smart grid monitoring is critical to enhancing resiliency and operational efficiency of power equipment. Cloud-based and edge-based fault detection systems integrating deep learning have been proposed recently to monitor the grid in real time. However, state-of-the-art cloud-based detection may require uploading a large amount of data and suffer from long network delay, while edge-based schemes do not adequately consider the detection requirement and thus cannot provide flexible and optimal performance. To solve these problems, we study a cloud-edge based hybrid smart grid fault detection system. Embedded devices are placed at the edge of the monitored equipment with several lightweight neural networks for fault detection. Considering limited communication resources, relatively low computation capabilities of edge devices, and different monitoring accuracies supported by these neural networks, we design an optimal communication and computational resource allocation method for this cloud-edge based smart grid fault detection system. Our method can maximize the processing throughput of the system and improve resource utilization while satisfying the data transmission and processing latency requirements. Extensive simulations are conducted and the results show the superiority of the proposed scheme over comparison schemes. We have also prototyped this system and verified its feasibility and performance in real-world scenarios.

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Index Terms

Resource Orchestration of Cloud-Edge–based Smart Grid Fault Detection
1. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 18, Issue 3

August 2022

480 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3531537

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 24 August 2022

Online AM: 04 April 2022

Accepted: 01 March 2022

Revised: 01 February 2022

Received: 01 September 2021

Published in TOSN Volume 18, Issue 3

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National Natural Science Foundation of China
Anhui Provincial Natural Science Foundation
Fundamental Research Funds for the Central Universities

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Ding XDing HZhou F(2024)Presenting a meta-heuristic solution for optimal resource allocation in fog computingJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23341846:4(11079-11094)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-233418
Chen YLi WHuang JGao HDeng S(2024)A Differential Evolution Offloading Strategy for Latency and Privacy Sensitive Tasks with Federated Local-edge-cloud CollaborationACM Transactions on Sensor Networks10.1145/3652515Online publication date: 12-Mar-2024
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Syu JLin JSrivastava G(2024)Distributed Learning Mechanisms for Anomaly Detection in Privacy-Aware Energy Grid Management SystemsACM Transactions on Sensor Networks10.1145/3640341Online publication date: 17-Jan-2024
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Li SBu RLi SLiu CHuang K(2024)Principal Properties Attention Matching for Partial Domain Adaptation in Fault DiagnosisIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.336626873(1-12)Online publication date: 2024
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