PROJECT TITLE :
Data-Driven Faulty Node Detection Scheme for Wireless Sensor Networks - 2017
ABSTRACT:
During this paper, a faulty node detection theme with a hybrid algorithm using a Markov chain model that performs collective monitoring of wireless sensor networks is proposed. Mostly wireless sensor networks are giant-scale systems, heavily noised, and also the system workload is unfairly distributed among the master node and slave nodes. Hence, the master node may not easily detect a faulty slave node. During this paper, a more correct faulty node detection scheme employing a Markov chain model is investigated. Each slave node's condition will be divided into three states by likelihood calculation: Sensible-,Warning-, and Bad-state. Using this data, the master node will predicts the realm in which a slip frequently happens. Simulation results show that the proposed technique can improve the reliability of faulty node detection and therefore the miss detection rate for a Wireless Sensor Networks.
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