Abstract—Air Pollution (AP) is one of the main threats to global health. Real-time dynamic mapping of pollution distribution is of a crucial importance to the AP reduction and management. Conventional air quality monitoring relies on expensive and cumbersome monitoring stations. Such stations are sparsely deployed over a region – typically one to a few per city. The extrapolation of the dynamic spatiotemporal data away from these stations might be inaccurate. In this paper, we present a participatory Vehicle Sensor Network (VSN) based on low-cost mobile nodes deployed on public (taxi) vehicles. The system enables continuous real-time data acquisition, transmission, and utilization. As compared to the conventional approaches, our system greatly improves sensing coverage. The proposed platform enables the acquisition of a large amount of georeferenced and time-stamped data. It provides real time pollution mapping and historical data view. The system’s operational stability and continuity are examined and confirmed through the analysis of background data collected during 15 days of experimental implementation.  
 

Keywords—air quality monitoring, machine learning, pollution, sensors, vehicle sensor network, wireless, wireless sensor network 

Cite: Bleron Zherka and Zhilbert Tafa, "A Vehicle Sensor Network for Real-Time Air  Pollution Analysis," Journal of Advances in Information Technology, Vol. 14, No. 1, pp. 39-45, February 2023.

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