Data-Driven Faulty Node Detection Scheme for Wireless Sensor Networks
Abstract
In this Data-Driven Faulty Node Detection Scheme for Wireless Sensor Networks paper, a more accurate faulty node detection scheme using a Markov chain model is investigated. Hence, the master node may not easily detect a faulty slave node. Each slave node’s condition can be divided into three states by probability calculation: Good-,Warning-, and Bad-state.
The master node can predict the area where an error frequently occurs using this information. Wireless sensor networks (WSN) have seen great improvement and utilization in the past few decades.
WSN can operate in remote areas without human intervention is a key factor in WSN’s success. WSN consist of spatially distributed autonomous sensor nodes that collaborate with each other. Sensor nodes are often deployed in hostile environments, such as industrial area, and even war area that make sensor nodes more susceptible to failures than other systems.
Failed sensor nodes may result in sensor network partitioning, reduced WSN availability, and WSN failure. In order to meet application requirements in the presence of sensor failures, fault detection mechanisms in WSN are imperative
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