Online Fault-Tolerant Dynamic Event Region Detection in Sensor Networks via Trust Model

TL;DR

This paper introduces a Bayesian trust-based model for online fault-tolerant detection of dynamic event regions in sensor networks, improving accuracy by accounting for node trustworthiness and spatiotemporal correlations.

Contribution

It presents a novel trust model integrated with particle filtering for real-time fault detection and event region identification in sensor networks.

Findings

Significantly reduces detection error rates.

Effectively discriminates faulty nodes from trustworthy ones.

Outperforms existing methods in dynamic event detection.

Abstract

This paper proposes a Bayesian modeling approach to address the problem of online fault-tolerant dynamic event region detection in wireless sensor networks. In our model every network node is associated with a virtual community and a trust index, which quantitatively measures the trustworthiness of this node in its community. If a sensor node's trust value is smaller than a threshold, it suggests that this node encounters a fault and thus its sensor reading can not be trusted at this moment. This concept of sensor node trust discriminates our model with the other alternatives, e.g.,the Markov random fields. The practical issues, including spatiotemporal correlations of neighbor nodes' sensor readings, the presence of sensor faults and the requirement of online processing are linked together by the concept trust and are all taken into account in the modeling stage. Based on the proposed model, the trust value of each node is updated online by a particle filter algorithm upon the arrival of new observations. The decision on whether a node is located in the event region is made based upon the current estimate of this node's trust value. Experimental results demonstrate that the proposed solution can provide striking better performance than existent methods in terms of error rate in detecting the event region.