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A Survey on Detecting Location-Based Faults in Wireless Sensor Networks Using Machine Learning and Deep Learning Techniques

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Proceedings of 3rd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 540))

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Abstract

A Wireless Sensor Network is a group of multiple detection stations referred as nodes which performs multiple actions (sensing, communicating and computing) in a collaborative manner. Wireless Sensor networks are broadly used in many applications which includes healthcare, defense, smart security systems and many more. But they are facing many issues with Fault Tolerance and reliability. Various investigations are going on for increasing the fault tolerance capabilities of WSNs so that they can be used in critical applications effectively. Different hard and soft faults occur in WSN due to several factors which commonly includes energy depletion, failure of communication link, hardware failure, dislocation of sensor nodes, radio interference, congestion problem, malicious attack, network lifetime problem, etc. The use of Machine Learning technique is a very effective way to overcome these faults as it is important to make the system fault-free. Machine Learning are self-learning process which works without re-programing and human interference. This paper presents the survey of Machine Learning, Deep Learning and Time Series methods used to detect anomalies, data centric faults and system centric faults in WSN which are one of the most commonly occurring type of faults in sensor networks.

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Authors and Affiliations

  1. BMS College of Engineering, Bangalore, Karnataka, India

    Neha Jagwani & G. Poornima

Authors
  1. Neha Jagwani
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  2. G. Poornima
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Corresponding author

Correspondence to Neha Jagwani .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vinit Kumar Gunjan

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Jagwani, N., Poornima, G. (2023). A Survey on Detecting Location-Based Faults in Wireless Sensor Networks Using Machine Learning and Deep Learning Techniques. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of 3rd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Lecture Notes in Networks and Systems, vol 540. Springer, Singapore. https://doi.org/10.1007/978-981-19-6088-8_43

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  • DOI: https://doi.org/10.1007/978-981-19-6088-8_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6087-1

  • Online ISBN: 978-981-19-6088-8

  • eBook Packages: EngineeringEngineering (R0)

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