Dynamic Noisy Measurement Aware Localization Model for Wireless Sensor Networks

Abstract

Localization through received signal strength (RSS) has attained a lot of interest across industries and research organization due to ease of use, high efficiency and low computation complexity; thus, it is widely used in Wireless Sensor Networks (WSNs)-based applications. Existing localization model has been predominantly designed with known transmit power. Recently, few localization approaches have been modeled considering unknown transmit power employing non-convex least squared relative error (LSRE) measurement model. The LSRE optimization problem is solved through semidefinite programming (SDP) using semidefinite relaxation (SDR). However, LSRE-SDP suffers immensely under highly dynamic and noisy environment and induces high computation overhead in meeting convergence. In addressing the aforementioned problem, this paper presents Dynamic Noisy Measurement Aware Localization (DNMAL) model for WSNs using improved least square bounding model. The objective DNMAL is to measure target position by neglecting the collected through noisy (faulty) sensor device. The DNMAL aids in achieving optimal solution using improved least square bounding model through iterative process. The DNMAL is efficient in bounding unknown distribution because of presence of noisy sensor and significantly reduces localization error even with presence of extreme noise.