Error minimizing jammer localization through smart estimation of ambient noise

Jammer can jeopardize the dependability of wireless networks, and jammer's position information allows the network to cope with jamming leveraging varieties of defense strategies. Thus, in this paper, we address the problem of localizing jammer. Prior work relies on indirect measurements derived from jamming effects, which makes it difficult to accurately localize jammer. We localize jammer by directly using the strength of jamming signals (JSS). Estimating JSS is challenging as they may be embedded in other signals. As such, we devise an estimation scheme based on ambient noise floor and validate it with real world experiments. To improve localization accuracy, we define an evaluation feedback metric to quantify the estimation errors and formulate jammer localization as a nonlinear optimization problem, whose optimal solution approaches jammer's true position. We exploit a heuristic search based algorithm for approximating the global optimal solution, and our extensive simulation shows that our error-minimizing-based algorithm outperforms existing algorithms.