Nonparametric Bootstrap-Based Multihop Localization Algorithm for Large-Scale Wireless Sensor Networks in Complex Environments

This paper presents a nonparametric bootstrap multihop localization algorithm for large-scale wireless sensor networks (WSNs) in complex environments. Unlike most of the existing schemes, this work is based on the consideration that it is not feasible to obtain a lot of available distance measurements sample for estimation and to get exact noise distributions or enough prior information for conventional statistical methods, which is a situation commonly encountered in complex environments practically. For the first time, we introduce a nonparametric bootstrap method into multihop localization to build confidence intervals for multihop distance estimation, which can eliminate the risk of small sample size and unknown distribution. On this basis, we integrate the interval analysis method with bootstrap approach for ordinary nodes localization. To reduce the computational complexity, boxes approach is utilized to approximate the irregular intersections. Simulation results show that our proposed scheme is less affected by the variation of unknown distributions and indicate that our method can achieve high localization coverage with relatively small average localization error in large-scale WSNs, especially in sparse and complex network with smaller connectivity and anchor percentage.