Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN

Abstract

Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach.