LAUSR

This article studies a class of distributed attack strategies against state estimation of wireless sensor networks. The attack objective is to corrupt the least numbers of sensors so that the state estimate error approaches a target bias while avoiding being detected and high attacking is lost. First, with precise knowledge of the measurement model and applying a sparsity projection operation with average linear complexity rather than the popular brute force search and approximation techniques, a distributed optimization scheme is proposed which provides an exact and low-complexity algorithmic solution to finding the optimal attack strategy. Furthermore, by introducing “dead-zone” type projection and region projection operators, a distributed optimization algorithm for robust sparse undetectable attacks is proposed with imprecise model knowledge. A distinguishing point of the proposed algorithms is that a tradeoff between computational complexity and an attack’s impact can be achieved by properly designing a cost function. Simulation results on an unmanned ground vehicle are presented to substantiate the algorithm.