RSSI-based Secure Localization in the Presence of Malicious Nodes in Sensor Networks

TL;DR

This paper introduces four novel secure localization techniques for sensor networks that effectively detect and mitigate malicious nodes, significantly improving accuracy in adversarial scenarios.

Contribution

It proposes four new algorithms, including WLS, SWLS, LN-1, and LN-1E, that enhance secure localization by detecting malicious nodes and handling coordinated attacks.

Findings

WLS and SWLS outperform existing methods by weighting closer anchor nodes more.

LN-1E effectively estimates two planes to counter coordinated malicious attacks.

Proposed techniques achieve higher localization accuracy than prior algorithms.

Abstract

The ability of a sensor node to determine its location in a sensor network is important in many applications. The infrastructure for the location-based services is an easy target for malicious attacks. We address scenarios where malicious node(s) attempt to disrupt, in an uncoordinated or coordinated manner, the localization process of a target node. We propose four techniques for secure localization: weighted least square (WLS), secure weighted least square (SWLS), and $\ell_1$-norm based techniques LN-1 and LN-1E, in a network that includes one or more compromised anchor nodes. WLS and SWLS techniques are shown to offer significant advantage over existing techniques by assigning larger weights to the anchor nodes that are closer to the target node, and by detecting the malicious nodes and eliminating their measurements from the localization process. In a coordinated attack, the localization problem can be posed as a plane fitting problem where the measurements from non-malicious and malicious anchor nodes lie on two different planes. LN-1E technique estimates these two planes and prevents disruption of the localization process. The Cramer-Rao lower bound (CRLB) for the position estimate is also derived. The proposed techniques are shown to provide better localization accuracy than the existing algorithms.