Simultaneous Perturbation Stochastic Approximation-based Localization Algorithms for Mobile Devices

Localization precision remains active and open challenge in the area of wireless networks. For static network we develop model free approach of localization technique that bypasses the tedious modeling of diverse aspects to the contributing factor of localization errors, namely simultaneous perturbation stochastic approximation (SPSA) localization technique. The improved version of SPSA, simultaneous perturbation stochastic approximation by neighbor confidence (SPSA-NC) addresses error propagation of iterative localization controlled by incorporating a neighbor confidence matrix. The centralized SPSA and SPSA-NC does not scale well for the mobile environment due to the messaging requirements of repeated updates. We take distributed approaches to implement the aforementioned localization techniques for mobile devices by distributed simultaneous perturbation stochastic approximation (DSPSA) and distributed simultaneous perturbation stochastic approximation by neighbor confidence (DSPSA-NC) respectively; compare the results with the centroid (C) and weighted centroid (WC) localization techniques and show superiority of our methods.