Self-localisation of IoT devices with imperfect measurements via approximate maximum-likelihood estimation
by Vikram Kumar; Reza Arablouei
International Journal of Sensor Networks (IJSNET), Vol. 46, No. 2, 2024

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.

Online publication date: Tue, 01-Oct-2024

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