Calibration of chemical sensors in mobile wireless networks

TL;DR

This paper introduces a novel opportunistic calibration method for mobile chemical sensors that exchanges measurements during encounters to correct sensor drift, enhancing pollutant monitoring accuracy in urban areas.

Contribution

It proposes a new calibration approach based on sensor encounters and weighted least-squares optimization with simulated annealing for decay constant tuning.

Findings

The method reduces calibration errors compared to baseline approaches.

Performance varies with network size and weight functions.

Simulated results validate the effectiveness of the approach.

Abstract

Low-power chemical sensors deployed on mobile platforms make it possible to monitor pollutant concentrations across large urban areas. However, chemical sensors are prone to drift (e.g., aging, damage, poisoning) and have to be calibrated periodically. In this paper, we present an opportunistic calibration approach that relies on encounters between sensors; when in vicinity of each other, sensors exchange measurements and use the accumulated information to re-calibrate. We formulate the calibration process as weighted least-squares, where the most recent measurements are assigned the highest weights. We model the weights with an exponential decay function (in time) and optimize the decay constant using simulated annealing (SA). We validated the proposed method on a simulated sensor network with the sensors' mobility driven by random-waypoint (RWP) models. We present results in terms of average calibration errors for different weight functions, and network sizes.