Data-Driven Faulty Node Detection Scheme for Wireless Sensor Networks - 2017 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Security Implications of Permission Models in Smart-Home Application Frameworks - 2017 Transient Community Detection and Its Application to Data Forwarding in Delay Tolerant Networks - 2017