Matrix Completion Based Localization in the Internet of Things Network

TL;DR

This paper introduces a matrix completion algorithm utilizing a modified conjugate gradient method to accurately reconstruct sensor locations in IoT networks from incomplete distance data.

Contribution

It presents a novel matrix completion approach tailored for IoT localization, improving the accuracy of sensor position estimation from partial observations.

Findings

Effective recovery of Euclidean distance matrix demonstrated

Algorithm outperforms traditional methods in partial data scenarios

Numerical experiments validate the approach's accuracy

Abstract

In order to make a proper reaction to the collected information from internet of things (IoT) devices, location information of things should be available at the data center. One challenge for the massive IoT networks is to identify the location map of whole sensor nodes from partially observed distance information. In this paper, we propose a matrix completion based localization algorithm to reconstruct the location map of sensors using partially observed distance information. From the numerical experiments, we show that the proposed method based on the modified conjugate gradient is effective in recovering the Euclidean distance matrix.