Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors

TL;DR

This paper presents a novel 3D cooperative localization method for low-cost IoT sensors using RSS measurements, combining convex relaxation and likelihood ascent search to achieve high accuracy efficiently.

Contribution

It introduces an integrated approach that combines continuous space localization with permutation likelihood ascent search, improving accuracy and efficiency over existing methods.

Findings

Achieves close-to-100% localization accuracy

Outperforms state-of-the-art algorithms in experiments

Provides an efficient solution for large-scale IoT sensor localization

Abstract

Many location-based services use Received Signal Strength (RSS) measurements due to their universal availability. In this paper, we study the association of a large number of low-cost Internet-of-Things (IoT) sensors and their possible installation locations, which can enable various sensing and automation-related applications. We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search. A convex relaxation-based optimization is designed to estimate the coarse locations of blindfolded devices in continuous 3D spaces, which are then projected to the feasible permutation space. An efficient Cram\'er-Rao Lower Bound based likelihood ascent search algorithm is proposed to refine the solution. Extensive experiments were conducted to evaluate the performance of the proposed approach, which show that the proposed approach significantly outperforms state-of-the-art combinatorial optimization algorithms and achieves close-to-100% accuracy with affordable execution time.