An Optimal Trust Aware Cluster Based Routing Protocol Using Fuzzy Based Trust Inference Model and Improved Evolutionary Particle Swarm Optimization in WBANs

Abstract

The wireless body sensor network (WBSN) an extensive of WSN is in charge for the detection of patient’s health concerned data. This monitored health data are essential to be routed to the sink (base station) in an effective way by approaching the routing technique. Routing of tremendous sensed data to the base station minimizes the life time of the network due to heavy traffic occurrence. The major concern of this work is to increase the lifespan of the network which is considered as a serious problem in the wireless network functionalities. In order to recover this issue, we propose an optimal trust aware cluster based routing technique in WBSN. The human body enforced for the detection of health status is assembled with sensor nodes. In this paper, three novel schemes namely, improved evolutionary particle swarm optimization (IEPSO), fuzzy based trust inference model, and self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) are proposed for the secured transmission of data. The sensor nodes are gathered to form a cluster and from the cluster, it is necessary to select the cluster head (CH) for the effective transmission of data to nearby nodes without accumulation. The CH is chosen by considering IEPSO algorithm. For securable routing, we exhibit fuzzy based trust inference model to select the trusted path. Finally, to reduce traffic occurrence in the network, we introduce SGBAS algorithm. Experimental results demonstrate that our proposed method attains better results when compared with conventional clustering protocols and in terms of some distinctive QoS determinant parameters.