Wireless localization with diffusion maps

Amin Ghafourian,Orestis Georgiou,Thilo Gross,Edmund Barter

doi:10.1038/s41598-020-77695-7

Abstract

In the Wireless Localization Matching Problem (WLMP) the challenge is to match pieces of equipment with a set of candidate locations based on wireless signal measurements taken by the pieces of equipment. This challenge is complicated by the noise that is inherent in wireless signal measurements. Here we propose the use of diffusion maps, a manifold learning technique, to obtain an embedding of positions and equipment coordinates in a space that enables coordinate comparison and reliable evaluation of assignment quality at very low computational cost. We show that the mapping is robust to noise and using diffusion maps allows for accurate matching in a realistic setting. This suggests that the diffusion-map-based approach could significantly increase the accuracy of wireless localization in applications.

Highlights

In the Wireless Localization Matching Problem (WLMP) the challenge is to match pieces of equipment with a set of candidate locations based on wireless signal measurements taken by the pieces of equipment
The bold vision of the Smart City is built around such Internet of Things (IoT) systems with billions or even trillions of interconnected devices and s ensors[1,2]
The most likely location of target devices are inferred through comparing signal features such as Received Signal Strength Indicator (RSSI) with a previously obtained database[8]

Summary

Introduction

In the Wireless Localization Matching Problem (WLMP) the challenge is to match pieces of equipment with a set of candidate locations based on wireless signal measurements taken by the pieces of equipment. This challenge is complicated by the noise that is inherent in wireless signal measurements. We show that the mapping is robust to noise and using diffusion maps allows for accurate matching in a realistic setting This suggests that the diffusion-map-based approach could significantly increase the accuracy of wireless localization in applications. The most likely location of target devices are inferred through comparing signal features such as Received Signal Strength Indicator (RSSI) with a previously obtained database[8]. In angle-based methods, the angle of arrival (AoA) of the signal from a number of antennas are obtained and the position of the device is estimated through t riangulation[16]

Methods

Results

Conclusion