FDMG: Fault detection method by using genetic algorithm in clustered wireless sensor networks

Abstract

Wireless sensor networks (WSNs) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or Sink. Since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, they are prone to failure. Thus, maintaining a network with its proper functions even when undesired events occur is necessary and is called fault tolerance. Hence, fault management is essential in these networks. In this paper, a new method has been proposed with particular attention to fault tolerance and fault detection in WSN. The performance of the proposed method was simulated in MATLAB. The proposed method was based on majority vote, which can permanently detect faulty sensor nodes accurately. High accuracy and low false alarm rate helped exclude them from the network. To investigate the efficiency of the new method, the researchers compared it with Chen, Lee, and hybrid algorithms. Simulation results indicated that the proposed method has better performance in parameters such as detection accuracy (DA) and a false alarm rate (FAR) even with a large set of faulty sensor nodes.

The Distributed Convergence Classifier Using the Finite Difference

“…The mechanism presented in [18] exploits a genetic algorithm in clustered WSNs. It is based on majority vote, which allows a permanent failure detection of the nodes.…”