Abstract
Localization is a highly important topic in wireless sensor networks as well as in many Internet of Things applications. Many current localization algorithms are based on the Sequential Monte Carlo Localization method (MCL), the accuracy of which is bounded by the radio range. High computational complexity in the sampling step is another issue of these approaches. We present Tri-MCL which significantly improves on the accuracy of the Monte Carlo Localization algorithm. To do this, we leverage three different distance measurement algorithms based on range-free approaches. Using these, we estimate the distances between unknown nodes and anchor nodes to perform more fine-grained filtering of the particles as well as for weighting the particles in the final estimation step of the algorithm. Simulation results illustrate that the proposed algorithm achieves better accuracy than the MCL and SA-MCL algorithms. Furthermore, it also exhibits high efficiency in the sampling step.