Reference-free Calibration in Sensor Networks
This addresses the challenge of reliable calibration in large-scale IoT networks, offering a solution that avoids single-point dependence, though it is incremental as it builds on existing calibration frameworks.
The paper tackles the problem of calibrating densely deployed sensor networks without relying on a single reference sensor, which can be problematic if the reference is erroneous. The proposed unbiased reference-free algorithms achieve asymptotic statistical lower bounds in simulations and show improvements over existing methods on real-life datasets.
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.