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Artificial neural networks-based improved Levenberg–Marquardt neural network for energy efficiency and anomaly detection in WSN

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Abstract

One of the key goals in the design of the networks is to increase the lifespan of wireless sensor networks (WSNs). Using different models of intelligent energy management could help designers achiseve this objective. By reducing the number of sensors required to collect data on the environment, these models can achieve higher levels of energy efficiency without sacrificing the quality of the readings. When battery power is an issue, wireless sensor networks (WSNs) are often employed for applications such as monitoring or tracking. Several routing protocols have been developed in the last several years as possible answers to this problem. Despite this, the issue of extending the lifetime of the network while considering the capacities of the sensors remain open. As a result of applying neural networks, Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy-Efficient Sensor Routing (EESR) can be improved in terms of their overall efficiency as well as their level of dependability, as is shown in this research EESR. Energy-Efficient Sensor Routing (ESR) and Low-Energy Adaptive Clustering Hierarchy (LEACH) are the names of the two protocols that are being utilized here EESR. The system incorporates a refined version of the Levenberg–Marquardt Neural Network (LMNN), which serves to enhance the efficiency with which it uses energy. The ability of an Intrusion Detection Systems (IDS) based on an artificial neural system to detect anomalies has also been proven. Anomalies can be identified using this system's optimum feature selection. Simulations showed that the proposed ANN-ILMNN model worked better, as shown by these results.

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Data availability statement

No datasets were generated or analyzed during the current study.

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Not applicable.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, P. E. S. College of Engineering, Mandya, India

    M. Revanesh

  2. Department of Electronics and Computer Engineering, Amrutvahini College of Engineering, Sangamner, Maharashtra, India

    Sheetal S. Gundal

  3. Department of Computer Science and Engineering, Modern Institute of Technology and Research Centre, Alwar, Rajasthan, India

    J. R. Arunkumar

  4. Department of ECE, Malla Reddy Engineering College, Hyderabad, Telangana, India

    P. Joel Josephson

  5. Department of CSE, R.M.D. Engineering College, Chennai, India

    S. Suhasini

  6. Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, India

    T. Kalavathi Devi

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  1. M. Revanesh
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  2. Sheetal S. Gundal
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  3. J. R. Arunkumar
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  4. P. Joel Josephson
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  5. S. Suhasini
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  6. T. Kalavathi Devi
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All author is contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

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Correspondence to M. Revanesh.

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Revanesh, M., Gundal, S.S., Arunkumar, J.R. et al. Artificial neural networks-based improved Levenberg–Marquardt neural network for energy efficiency and anomaly detection in WSN. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03297-6

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  • DOI: https://doi.org/10.1007/s11276-023-03297-6

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