RSS-Based Localization in the Presence of Malicious Nodes in Sensor Networks

Abstract

The ability of a sensor node to determine its location in a sensor network is important in many applications. We address the scenario, where malicious anchor node(s) attempt to disrupt, in an uncoordinated or coordinated manner, the localization process of a target node. We propose four localization techniques: weighted least square (WLS), secure WLS (SWLS), and $\ell _{1}$ -norm-based techniques LN-1 and LN-1E. WLS and SWLS techniques are shown to offer a significant advantage in uncoordinated attacks over existing techniques by detecting the malicious nodes and eliminating their measurements from the localization process and assigning larger weights to the measurements corresponding to anchor nodes that are closer to the target node. In a coordinated attack, the localization problem can be posed as a plane fitting problem, where the measurements from nonmalicious and malicious anchor nodes lie on two different planes. The LN-1E technique estimates the two planes and prevents disruption of the localization process. LN-1E can also determine the location where the malicious anchor nodes intend to make the target node appear to be located. The Cramer–Rao lower bound for the position estimate is also derived. We present simulation and experimental results, which demonstrate that the proposed techniques provide better localization accuracy than existing algorithms.