Robust in-situ data reconstruction from poisson noise for low-cost, mobile, non-expert environmental sensing

Abstract

Personal and participatory environmental sensing, especially of air quality, is a topic of increasing importance. However, as the employed sensors are often cheap, they are prone to erroneous readings, e.g. due to sensor aging or low selectivity. Additionally, non-expert users make mistakes when handling equipment. We present an elegant approach that deals with such problems on the sensor level. Instead of characterizing systematic errors to remove them from the noisy signal, we reconstruct the true signal solely from its Poisson noise. Our approach can be applied to data from any phenomenon that can be modeled as particles and is robust against both offset and drift, as well to a certain extent against cross-sensitivity. We show its validity on two real-world datasets.