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Long-short term anomaly detection in wireless sensor networks based on spatio-temporal correlation in IoT systems

Published: 14 June 2024 Publication History

Abstract

For the large number of different information sources collected by heterogeneous wireless sensor networks, traditional anomaly detection frameworks usually focus on the data analysis itself and lack a focus on unexpected sensor data, leading to unnecessary waste of resources. To address this problem, this paper proposes a method for automatic anomaly detection in heterogeneous sensor networks that combines edge data analysis and cloud data analysis. The former utilizes a completely unsupervised Isolation forest algorithm, while the cloud data analysis utilizes a Long-short term memory neural network algorithm. Experimental evaluation is performed by discussing the analysis of the proposed method using discrete and event outlier data. The experimental results show that the proposed method can reasonably cope with discrete outliers and event outliers, and the detection accuracy of event outliers is improved from 93.60% to 98.91%. At the same time, the Edge-Cloud combination alleviates to a certain extent the drawback of high energy consumption and latency of the traditional machine learning algorithm local combination model.

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AIPR '23: Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2024

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Author Tags

  1. Anomaly detection
  2. Edge computing
  3. Isolation forests
  4. Spatio-temporal correlation

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • the Scientific Research Program of the Science and Technology Department of Shaanxi Province
  • the Scientific Research Program of Shaanxi Provincial Education Department
  • the Scientific Research Program of the Science and Technology Bureau of Yulin
  • the Shaanxi Province Qinchuangyuan Scientist + Engineer Team Construction Project
  • the Scientific Research Program of the Science and Technology Bureau of Xi'an

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AIPR 2023

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