Weight Red Deer Algorithm Based Clustering Selection And Fuzzy Trust Evaluation For Wireless Sensor Networks

Main Article Content

A.Prakash
M prakash

Keywords

Wireless Sensor Networks (WSNs), fuzzy trust evaluation, clustering, Cluster Head (CH) selection, security, K-Means Clustering (KMC),Weight Red Deer Algorithm (WRDA)

Abstract

Wireless Sensor Networks (WSNs) have received the most interest owing to their vast variety of applications.WSNs detect air pollution, humidity, and temperature, and seismic event detections. They are made up of thousands of sensor nodes that interact with one another.The primary issues with the WSN in the current system are security and energy usage. Additionally, they are unable to defend against attacks from compromised or self-centered internal nodes with proper identities.Weight Red Deer Algorithm (WRDA) is suggested in this study as a solution to the aforementioned issue.The system model, fuzzy trust assessment, outlier identification, and CH node selection are the primary stages of this study.Fuzzy trust evaluation is initially used to translate transmission evidences into trust values and minimise trust uncertainty.A K-Means-based outlier identification approach is then presented to analyse a large number of trust values from fuzzy trust assessment or trust recommendation.A meta-heuristic-based secure clustering technique is provided to balance sensor node security and energy savings while selecting CHs.Energy, neighbours, and node base station distance, and security assurance are among the parameters utilized in the WRDA strategy to choose CHs.CHs at the intermediate layer establish the routing backbone to collect, integrate, and transmit data from member nodes. The simulation reveals that the proposed WRDA architecture outperforms existing techniques in throughput, network lifetime, data transfer rate, and energy use.

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