An Attack-resistant RSS-based Localization Algorithm with L1 Regularization for Wireless Sensor Networks

A310-resistant localization in the presence of malicious nodes is an important issue in wireless sensor networks (WSNs). In this paper, an attack-resistant localization algorithm based on L1 regularization is proposed. By introducing unknown perturbations into the traditional path loss model, attack model is built and the sparsity of malicious attacks is exploited to formulate the secure localization problem as a nonlinear constrained optimization problem. This model allows estimating locations and recognizing attacks simultaneously. To solve the problem, the projected gradient search algorithm with Backtracking-Armijo line search is adopted. Finally, the simulation results show that the proposed algorithms can achieve higher localization accuracy than nonlinear least square method in the presence of malicious nodes, and the proposed method or the improved one can be adaptively chosen in terms of energy and accuracy requirements.