Deep Learning and Matrix Completion-aided IoT Network Localization in the Outlier Scenarios

TL;DR

This paper introduces a deep learning and matrix completion method for accurate IoT network localization in outlier scenarios, focusing on recovering Euclidean distance matrices and sensor positions.

Contribution

It presents a novel joint deep learning approach that incorporates matrix properties and outlier modeling for improved localization accuracy.

Findings

Accurately recovers sensor locations despite outliers

Effectively models outliers as sparse matrices

Demonstrates superior performance over traditional methods

Abstract

In this paper, we propose a deep learning and matrix completion aided approach for recovering an outlier contaminated Euclidean distance matrix D in IoT network localization. Unlike conventional localization techniques that search the solution over a whole set of matrices, the proposed technique restricts the search to the set of Euclidean distance matrices. Specifically, we express D as a function of the sensor coordinate matrix X that inherently satisfies the unique properties of D, and then jointly recover D and X using a deep neural network. To handle outliers effectively, we model them as a sparse matrix L and add a regularization term of L into the optimization problem. We then solve the problem by alternately updating X, D, and L. Numerical experiments demonstrate that the proposed technique can recover the location information of sensors accurately even in the presence of outliers.