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Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks

Author

Listed:
  • Mohit Mittal

    (Centre de Recherche en Informatique, Signal et Automatique de Lille, INRIA, 59655 Villeneuve-d’Ascq, France)

  • Rocío Pérez de Prado

    (Telecommunication Engineering Department, University of Jaén, 23071 Jaén, Spain)

  • Yukiko Kawai

    (Department of Information Science and Engineering, Kyoto Sangyo University, Kamingamo, Kita-ku, Kyoto 603-8555, Japan
    Cybermedia Center (CMC), Osaka University, Osaka 565-0871, Japan)

  • Shinsuke Nakajima

    (Department of Information Science and Engineering, Kyoto Sangyo University, Kamingamo, Kita-ku, Kyoto 603-8555, Japan)

  • José E. Muñoz-Expósito

    (Telecommunication Engineering Department, University of Jaén, 23071 Jaén, Spain)

Abstract

Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy–Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg–Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub-cluster LEACH-derived protocol is also proposed. Simulation results show that the Sub-LEACH with LMNN outperformed its competitors in energy efficiency. In addition, the end-to-end delay was evaluated, and Sub-LEACH protocol proved to be the best among existing strategies. Moreover, an intrusion detection system (IDS) has been proposed for anomaly detection based on the support vector machine (SVM) approach for optimal feature selection. Results showed a 96.15% accuracy—again outperforming existing IDS models. Therefore, satisfactory results in terms of energy efficiency, end-to-end delay and anomaly detection analysis were attained.

Suggested Citation

  • Mohit Mittal & Rocío Pérez de Prado & Yukiko Kawai & Shinsuke Nakajima & José E. Muñoz-Expósito, 2021. "Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks," Energies, MDPI, vol. 14(11), pages 1-21, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3125-:d:563231
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    Citations

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    Cited by:

    1. Mudassir Khan & A. Ilavendhan & C. Nelson Kennedy Babu & Vishal Jain & S. B. Goyal & Chaman Verma & Calin Ovidiu Safirescu & Traian Candin Mihaltan, 2022. "Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN," Energies, MDPI, vol. 15(13), pages 1-14, June.
    2. Wojciech Szczepanik & Marcin Niemiec, 2022. "Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    3. Vladimir Shakhov & Olga Sokolova & Insoo Koo, 2021. "On the Suitability of Intrusion Detection System for Wireless Edge Networks," Energies, MDPI, vol. 14(18), pages 1-13, September.

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