Reference-free Calibration in Sensor Networks

TL;DR

This paper introduces unbiased reference-free calibration algorithms for dense sensor networks, improving robustness and accuracy over traditional reference-dependent methods, with proven asymptotic optimality and real-world performance gains.

Contribution

It proposes novel reference-free calibration algorithms that avoid reliance on a single sensor reference, enhancing robustness and accuracy in large-scale sensor networks.

Findings

Achieve asymptotic statistical lower bounds

Show improved performance on real datasets

Allow incorporation of additional references if available

Abstract

Sensor calibration is one of the fundamental challenges in large-scale IoT networks. In this article, we address the challenge of reference-free calibration of a densely deployed sensor network. Conventionally, to calibrate an in-place sensor network (or sensor array), a reference is arbitrarily chosen with or without prior information on sensor performance. However, an arbitrary selection of a reference could prove fatal, if an erroneous sensor is inadvertently chosen. To avert single point of dependence, and to improve estimator performance, we propose unbiased reference-free algorithms. Although, our focus is on reference-free solutions, the proposed framework, allows the incorporation of additional references, if available. We show with the help of simulations that the proposed solutions achieve the derived statistical lower bounds asymptotically. In addition, the proposed algorithms show improvements on real-life datasets, as compared to prevalent algorithms.