In this letter, we consider the localization problem using received signal strength in wireless sensor networks. Working with a simple non-cooperative scenario in an outdoor localization, we transform the received signal strength measurement model to an alternative optimization problem which is much easier to solve and less complex compared to finding the optimum solutions from the maximum likelihood estimator. Then, we can solve a sequence of nonconvex problems as a range constrainted optimization problem, while the estimated solution also guarantees a monotonic convergence to the original solution. Simulation results confirm the effectiveness of our proposed approach.

Secure sensor localization is a prerequisite for many sensor networks to retrieve trustworthy data. However, most of existing node positioning systems were studied in trust environment and are therefore vulnerable to malicious attacks. In this work, we develop a robust node positioning mechanism(ROPM) to protect localization techniques from position attacks. Instead of introducing countermeasures for every possible internal or external attack, our approach aims at making node positioning system attack-tolerant by removing malicious beacons. We defeat internal attackers and external attackers by applying different strategies, which not only achieves robustness to attacks but also dramatically reduces the computation overhead. Finally, we provide detailed theoretical analysis and simulations to evaluate the proposed technique.

This letter presents the variation of localization error to network parameters, the number of range estimation results from anchor nodes (ANs) and average distance between ANs in centralized Wireless Sensor Network (WSN). In sensor network, ANs estimate the relative range to Target Node (TN) using Time-Of-Arrival (TOA) information of Ultra WideBand (UWB) radio and a fusion center determines the final localization of TN based on estimation results reported. From simulation results, the variation of localization error, which is defined as the difference between localization result of TN and its actual location, is represented as the function of number of estimation results to average distance between ANs. The distribution of localization error is matched to the Rician distribution whose K-factor value is given by the proposed formula as well. Finally, the normalized error function for the efficient localization network design is characterized.