Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

TL;DR

This paper develops a novel distributed fault detection method for wireless sensor networks, explicitly incorporating error probabilities and using Bayesian model averaging to improve fault identification accuracy in noisy environments.

Contribution

It introduces a new detection probability for adjacent sensors and applies model selection and Bayesian averaging for enhanced fault detection in WSNs.

Findings

Incorporates error probabilities into fault detection schemes.

Introduces a new detection probability for adjacent sensors.

Uses Bayesian model averaging to improve fault identification.

Abstract

Sensor networks aim at monitoring their surroundings for event detection and object tracking. But, due to failure, or death of sensors, false signal can be transmitted. In this paper, we consider the problems of distributed fault detection in wireless sensor network (WSN). In particular, we consider how to take decision regarding fault detection in a noisy environment as a result of false detection or false response of event by some sensors, where the sensors are placed at the center of regular hexagons and the event can occur at only one hexagon. We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process. We introduce two types of detection probabilities, one for the center node, where the event occurs and the other one for the adjacent nodes. This second type of detection probability is new in sensor network literature. We develop schemes under the model selection procedure, multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error.