Performance Analysis of Dynamic Wireless Sensor Networks using Linguistic Fuzzy

Abstract

sensor networks (WSNs) are becoming very popular due to their large use in many of applications such as monitoring and collecting data from undisturbed dangerous environments. But the nodes in a sensor network are severely affected by energy. Reducing energy consumption of nodes to increase the network lifetime is considered as a most important challenge, so this paper will simulate the Linguistic Fuzzy Trust Model (LFTM) over dynamic Wireless Sensor Networks to save energy and shows the effect of dynamics in the performance of the model. A comparison in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length is also presented between LFTM model over dynamic WSNs and LFTM model over static WSNs. Also in this paper, a comparison between the Linguistic Fuzzy Trust Model (LFTM) and the Bio- inspired Trust and Reputation Model for Wireless Sensor Networks (BTRM-WSN) is achieved in terms of the accuracy and the average path length. Both models will give quite good and accurate outcomes over dynamic Wireless Sensor Networks.