Mobile Sensor Network Design and Optimization for Air Quality Monitoring

Abstract

Air quality and personal pollutant exposure measurement are important for the health and productivity of individuals. Accurate measurement of personal exposure is challenging because of the spatially and temporally heterogeneous distribution of pollutant concentrations. We propose to use low-cost and miniature mobile sensor networks to provide real-time measurement of the environment directly surrounding the user. However, there are many challenges, including sensor drift, cross sensitivity, and noises, to be addressed before mobile sensor network can be deployed in large scale and real-world applications.

My thesis aims to address those challenges by designing prototype sensor nodes of future generation mobile sensor networks, developing optimization techniques and systems, and evaluating the mobile sensor network in real-world deployments. My efforts can be divided into four categories: (1) we design the mobile sensor nodes and the mobile sensor network architecture that are capable of automatically collecting environment data and transferring them to a database; (2) we model the sensor drift based on measurement and develop techniques such as collaborative calibration and optimal human mobility-aware sensor placement to minimize the drift error of individual sensors; (3) we model the pollutant concentration in indoor environment considering inaccurate sensors and based on the model, we develop a hybrid sensor network synthesis technique to design accurate sensor networks under a cost constraint; and (4) we propose a Bayesian network based sensor noise reduction system that can correct abnormal sensor readings, re-calibrate the sensor functions, and identify the gas composition is the environment simultaneously. All the techniques are evaluated and validated using the data collected from real-world deployment. Experimental and simulation results show that our technique can reduce drift error significantly. For example, compared with the closest technique, our collaborative calibration technique can reduce sensor network error by 23.2%; our hybrid sensor network synthesis technique can improve the result by 35.8%; and our noise reduction technique can outperform the existing technique by 34.1%.