Eavesdropper and Jammer Selection in Wireless Source Localization Networks
Cuneyd Ozturk, Sinan Gezici

TL;DR
This paper investigates optimal placement strategies for eavesdropper and jammer nodes in wireless source localization networks, aiming to maximize eavesdropping accuracy and degrade localization performance, using convex optimization techniques.
Contribution
It introduces a convex optimization framework for eavesdropper and jammer selection, including robust versions under parameter uncertainty, and explores joint placement strategies.
Findings
Convex relaxation effectively approximates the selection problems.
Proposed algorithms outperform baseline methods in simulations.
Joint eavesdropper and jammer placement enhances adversarial effectiveness.
Abstract
We consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We first propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions in the network to estimate the target node position as accurately as possible. As the performance metric, the Cramer-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection…
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