An Improved Particle-Filter-Based Hybrid Optimization Algorithm for IoT Positioning in Uncertain WSNs

Abstract

With the wide applications of the Internet of Things (IoT) technologies in intelligent manufacturing, production safety, smart cities and other fields, target location awareness has been the primary issue as it can be used to personnel and material positioning, electronic fence settings, daily attendance statistics, and so on. Wireless Sensor Networks (WSNs) positioning can become an indispensable part of these location-based IoT applications that benefit from the ubiquitous sensing and communication ability. For addressing the large positioning errors caused by uncertain WSNs, this paper proposes an improved particle filter-based hybrid optimization (IPFHO) algorithm. After mapping the noisy wireless signal to uncertain target location, the preliminary positioning of mobile target is realized by improved particle filter, whose coarse accuracy over time can be further optimized with use of iterative search method. We evaluate the proposed algorithm under different noise levels, process noise variances and computation times in extensive simulations. The results indicate that the positioning accuracy of proposed algorithm can be effectively improved in the presence of wireless ranging errors and anchor node calibration errors. Compared with the positioning error 0.27m estimated by pure filtering, the positioning error of proposed algorithm can be reduced to 0.19m by combining the filtering and search methods. The platform experimental results, which are consistent with the trend of the simulation results, validate the superior accuracy of proposed algorithm compared with relevant positioning algorithms.