Title: Self-localisation of IoT devices with imperfect measurements via approximate maximum-likelihood estimation

Authors: Vikram Kumar; Reza Arablouei

Addresses: School of Computing, IIIT Una, Himachal Pradesh – 177209, India ' CSIRO's Data61, Pullenvale QLD 4069, Australia

Abstract: We address the challenge of self-localisation encountered by resource-constrained IoT devices when only a single set of perturbed (noisy) values of RSSI and anchor position estimates are available from nearby anchor nodes. Existing algorithms typically employ maximum-likelihood (ML) estimation to jointly localise both the self-localising blind node and the anchor nodes. However, this approach introduces unnecessary complexity by necessitating the localisation of the anchor nodes. To enhance resource efficiency and eliminate the need for estimating the anchor node positions, we propose approximating the objective function of the underlying ML estimation problem with an appropriate weighted least-squares cost function. Our proposed algorithm, designed to solve this modified problem, offers a substantial speedup of up to 10-fold compared to a state-of-the-art algorithm, while maintaining similar or even superior localisation accuracy. This renders the proposed algorithm particularly suitable for resource-constrained IoT devices, effectively addressing a critical need in IoT localisation.

Keywords: internet of things; IoT; maximum-likelihood estimation; Cramer-Rao lower bound; received signal strength indicator; RSSI; self-localisation; multilateration; weighted least-squares; wireless sensor networks; WSNs.

DOI: 10.1504/IJSNET.2024.141787

International Journal of Sensor Networks, 2024 Vol.46 No.2, pp.87 - 99

Received: 20 Apr 2023
Accepted: 26 Feb 2024
Published online: 01 Oct 2024 *

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