Identifying Cheating Anchor Nodes using Maximum Likelihood and Mahalanobis Distance

TL;DR

This paper proposes a method combining trilateration, maximum likelihood estimation, and Mahalanobis distance to accurately identify malicious anchor nodes in Wireless Sensor Networks, reducing localization errors.

Contribution

It introduces a novel approach integrating trilateration with MLE and Mahalanobis distance for effective detection of cheating anchor nodes.

Findings

Significant reduction in localization error.

Effective detection of malicious anchor nodes.

Validated through ns-3 network simulations.

Abstract

Malicious anchor nodes will constantly hinder genuine and appropriate localization. Discovering the malicious or vulnerable anchor node is an essential problem in Wireless Sensor Networks (WSNs). In wireless sensor networks, anchor nodes are the nodes that know its current location. Neighbouring nodes or non-anchor nodes calculate its location (or its location reference) with the help of anchor nodes. Ingenuous localization is not possible in the presence of a cheating anchor node or a cheating node. Nowadays, it's a challenging task to identify the cheating anchor node or cheating node in a network. Even after finding out the location of the cheating anchor node, there is no assurance, that the identified node is legitimate or not. This paper aims to localize the cheating anchor nodes using trilateration algorithm and later associate it with maximum likelihood expectation technique (MLE), and Mahalanobis distance to obtain maximum accuracy in identifying malicious or cheating anchor nodes during localization. We were able to attain a considerable reduction in the error achieved during localization. For implementation purpose we simulated our scheme using ns-3 network simulator.