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Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey

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Proceedings of Third International Conference on Communication, Computing and Electronics Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 844))

Abstract

In this article, we survey the different data-driven techniques for smart anomaly detection at the Internet of Things (IoT) edge. Anomaly detection is a significant study issue because of its wide arrangement of use case, from data analytics to network protection, national security, low-cost solutions, and industrial automation. Since IoT sensors are dynamic in nature, they produce huge volume and multi-dimensional data, it is a complex process to detect anomaly. Machine learning-based edge computing can rectify the difficulties in IoT like network traffic, latency, and security, by automated response and shifting calculation physically nearer to the device edge where the information are generated. Our survey discovered several problems in research and conflicts using data-driven-based abnormality detection techniques for limited gadgets in genuine problems of IoT. Based on our findings, researchers may acquaint themselves with current methods, apply them to real-world issues, and grant to the growth of smart anomaly detection techniques.

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Authors and Affiliations

  1. SRM Institute of Science and Technology, Chennai, India

    J. Manokaran & G. Vairavel

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  1. J. Manokaran
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  2. G. Vairavel
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Correspondence to J. Manokaran .

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Editors and Affiliations

  1. Department of ECE, PPG Institute of Technology, Coimbatore, India

    V. Bindhu

  2. Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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Manokaran, J., Vairavel, G. (2022). Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey. In: Bindhu, V., Tavares, J.M.R.S., Du, KL. (eds) Proceedings of Third International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-16-8862-1_45

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  • DOI: https://doi.org/10.1007/978-981-16-8862-1_45

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  • Online ISBN: 978-981-16-8862-1

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