Sensors Augmentation Influence Over Trust and Reputation Models Realization for Dense Wireless Sensor Networks

This paper reports the power consumption and resource utilization of a trust and reputation model deployed in a wireless sensor network (WSN). The impact of sensor augmentation parameter for static and dynamic WSN with trust and reputation models has been examined. In particular, we present a novel WSN framework-based investigations on peer trust and linguistic fuzzy trust model (LFTM) for trust and reputation models. Accuracy, path length, and energy consumption of sensor node operations are reported for their current and average scenarios. In addition, we emphasized over the satisfaction evaluation for LFTM model in the deployed WSN framework. The performance of the sensor augmentation for our proposed framework is evaluated via analytic bounds and numerical simulations. Analytical results together with simulation results exhibit the eminence of the proposed sensor augmentation-based realization over past trust and reputation model investigations.