Estimating Distances via Received Signal Strength and Connectivity in Wireless Sensor Networks

TL;DR

This paper introduces a fusion method combining RSS and connectivity data using MLE to accurately estimate distances between nodes in wireless sensor networks, outperforming individual methods.

Contribution

A novel fusion approach based on MLE that integrates RSS and connectivity data for improved distance estimation in WSNs under practical conditions.

Findings

The proposed method outperforms individual RSS or connectivity approaches.

The method approaches the Cramer-Rao lower bound in performance.

Experimental results validate the effectiveness of the fusion technique.

Abstract

Distance estimation is vital for localization and many other applications in wireless sensor networks (WSNs). Particularly, it is desirable to implement distance estimation as well as localization without using specific hardware in low-cost WSNs. As such, both the received signal strength (RSS) based approach and the connectivity based approach have gained much attention. The RSS based approach is suitable for estimating short distances, whereas the connectivity based approach obtains relatively good performance for estimating long distances. Considering the complementary features of these two approaches, we propose a fusion method based on the maximum-likelihood estimator (MLE) to estimate the distance between any pair of neighboring nodes in a WSN through efficiently fusing the information from the RSS and local connectivity. Additionally, the method is reported under the practical log-normal shadowing model, and the associated Cramer-Rao lower bound (CRLB) is also derived for performance analysis. Both simulations and experiments based on practical measurements are carried out, and demonstrate that the proposed method outperforms any single approach and approaches to the CRLB as well.