Abstract

Frequent sensor calibration is essential in sensor networks with low-cost sensors. We exploit the fact that temporally and spatially close measurements of different sensors measuring the same phe-nomenon are similar. Hence, when calibrating a sensor, we ad-just its calibration parameters to minimize the differences between co-located measurements of previously calibrated sensors. In turn, freshly calibrated sensors can now be used to calibrate other sen-sors in the network, referred to as multi-hop calibration. We are the first to study multi-hop calibration with respect to a reference signal (micro-calibration) in detail. We show that ordi-nary least squares regression—commonly used to calibrate noisy sensors—suffers from significant error accumulation over multiple hops. In this paper, we propose a novel multi-hop calibration al-gorithm using geometric mean regression, which (i) highly reduces error propagation in the network, (ii) distinctly outperforms ordi-nary least squares in the multi-hop scenario, and (iii) requires con-siderably fewer ground truth measurements compared to existing network calibration algorithms. The proposed algorithm is espe-cially valuable when calibrating large networks of heterogeneous sensors with different noise characteristics. We provide theoretical justifications for our claims. Then, we conduct a detailed analy-sis with artificial data to study calibration accuracy under various settings and to identify different error sources. Finally, we use our algorithm to accurately calibrate 13 million temperature, ground ozone (O3), and carbon monoxide (CO) measurements gathered by our mobile air pollution monitoring network. 1.