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Reference-Free Calibration in Sensor Networks
Journal article   Open access   Peer reviewed

Reference-Free Calibration in Sensor Networks

Raj Thilak Rajan, Rob-van Schaijk, Anup Das, Jac Romme and Frank Pasveer
IEEE sensors letters, v 2(3), pp 1-4
01 Sep 2018
DOI: https://doi.org/10.1109/LSENS.2018.2866627
url
http://arxiv.org/abs/1805.11999View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Engineering Engineering, Electrical & Electronic Instruments & Instrumentation Physical Sciences Physics Physics, Applied Science & Technology Technology
Sensor calibration is one of the fundamental challenges in large-scale Internet of Things 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.

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6 Record Views
5 citations in Web of Science
6 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Electrical & Electronic
Instruments & Instrumentation
Physics, Applied
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