Location Region Estimation for Internet of Things: A Distance Distribution-Based Approach

Location region estimation (LRE) is a key issue for many location-based applications in the Internet of Things era. This paper explores the problem of accurate LRE (ALRE) with distance distribution methods. First, in order to capture the uncertainties during the distance ranging process, a disk error model is introduced by modeling the target as a random node inside a disk region. Then, a disk error-based ranging (DEBR) approach is designed and analyzed by proving that the parameter estimation of DEBR is unbiased. Furthermore, an ALRE algorithm is developed through taking into account both DEBR and the classical multilateration method. It is proved that the estimated region obtained by ALRE is tighter than that obtained by the traditional estimation method. In addition, extensive simulations are conducted to verify the unbiased estimation of DEBR and evaluate the performance of ALRE.