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Trust aware secured energy efficient fuzzy clustering-based protocol in wireless sensor networks

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Abstract

Recent advancements in Wireless Sensor Networks (WSNs) have generated interest in the field of sensor tracking events. Environment monitoring is one of the most important applications where WSNs are used to guarantee animal conservation. WSNs are crucial in many real-time applications because of their topology, scale, and communication capabilities. Data privacy and integrity are the essential components that maximize network, security, trust and effectiveness. The fundamental problem with WSNs is the effective communication and efficient information to exchange among several sensor nodes for extending the lifespan of the WSNs. Moreover, along with these issues, it is necessary to measure an optimal path with the highest degree of reliability. In this paper, a new fuzzy and secured clustering algorithm is proposed for improving the energy efficiency and security. In the proposed method, clustering is proved to be the best strategy for energy management of WSN metrics because it uses trust-based fuzzy logic to find malicious nodes and selecting reliable path for data communication. The simulation results show that the proposed technique detects progress in relation to a variety of performance parameters. When compared to other various existing protocols, the proposed work reduces energy consumption while increasing network lifetime and security of communication.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    Bhukya Kranthikumar & R. Leela Velusamy

Authors
  1. Bhukya Kranthikumar
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  2. R. Leela Velusamy
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Contributions

BK has deigned the algorithms and LV has carried out literature survey and perform simulation for our proposed system.

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Correspondence to Bhukya Kranthikumar.

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Kranthikumar, B., Leela Velusamy, R. Trust aware secured energy efficient fuzzy clustering-based protocol in wireless sensor networks. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08098-9

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