Extending the Lifetime of Wireless Sensor Networks Using Fuzzy Clustering Algorithm Based on Trust Model

Subject Areas : Fuzzy logic, fuzzy set theory, and many-valued logic

Farshad Kiyoumarsi ^{1 *} , Behzad Zamani Dehkordi ²

1 - Department of Engineering, Faculty of Computer, Shahrekord Branch, Islamic Azad University, Shahrekord,Iran
2 - Department of Engineering, Faculty of Computer, Shahrekord Branch, Islamic Azad University, Shahrekord,Iran

Received: 2023-10-06 Accepted : 2023-10-06 Published : 2023-10-10

Keywords: Cluster head, Fuzzy clustering, Network lifetime, Trust model, Wireless sensor network.,

Abstract :

Wireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very important features of wireless sensor networks is their lifetime. Two important factors come to mind to increase the lifetime of networks: These factors are maintaining the coverage of the network and reducing the energy consumption of sensor nodes simultaneously with the uniform consumption of energy by all of them. Clustering, as the optimal method of data collection, is used to reduce energy consumption and maintain the coverage of the network in wireless sensor networks. In clustered networks, each node transmits acquired data to the cluster head to which it belongs. After a cluster head collects all the data from all member nodes, it transmits the data to the base station (sink). Given that fuzzy logic is a good alternative for complex mathematical systems, in this study, a fuzzy logic-based trust model uses the clustering method in wireless sensor networks. In this way, cluster-head sensors are elected from among sensors with high reliability with the help of fuzzy rules. As a result, the best and most trusted sensors will be selected as the cluster heads. The simulation results in MATLAB software show that in this way, in comparison with K-Means, FCM, subtractive clustering, and multi-objective fuzzy clustering protocols, the energy consumption in clustered nodes will decrease and the network’s lifetime will increase.

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