Genetic algorithm based sensor node classifications in wireless body area networks (WBAN)

Wireless body area network (WBAN) is a promising methodology in present health care systems to monitor, detect, predict and diagnose the disease in people. The performance of the WBAN network is affected by un-trusted nodes in WBAN network. The un-trusted sensor nodes are formed in WBAN network due to the attackers from outside the world. In this paper, sensor node classification algorithm is proposed which incorporates ANFIS classifier based trusted and un-trusted sensor nodes detection and classification system is proposed inorder to improve the efficiency of the WBAN networks. This proposed system constitutes feature extraction and classification modules. The trust features are extracted from sensor nodes and these exracted features are optimized using genetic algorithm. The performance of the WBAN network is analyzed in terms of classification rate, packet delivery ratio and latency.