Machine Learning (ML)-Based Air Quality Monitoring Using Vehicular Sensor Networks

Due to its advantages of providing a large geographical coverage and having no strict limits on energy and sensing and processing capabilities, a vehicular sensor network (VSN) has recently emerged as a promising paradigm for air quality monitoring in an urban area. However, designing an efficient VSN-based air monitoring system has challenges due to the vehicles' heterogeneous temporal and spatial coverage and the relatively expensive communication cost over cellular networks. In this paper, we propose a machine learning (ML)-based Air quality Monitoring (MLAirM) system which aims at reducing communication and sensing costs by allowing vehicles to process the collected data in a distributed fashion. More specifically, in MLAirM, vehicles are first assigned to take measurements at sets of locations in a sensing area. The vehicle then utilizes a distributed machine learning algorithm to learn a local model of air quality based on its collected data. Finally, the vehicle sends parameters of its model to a monitoring center which combines multiple local models to build a global air quality map. Furthermore, assigning the sensing locations to vehicles can be viewed as a successful measurement probability aware location assignment problem. An integer linear optimization problem is formulated and a heuristic algorithm is proposed to find the solution. Simulations based on realistic vehicular traces are performed to compare the proposed MLAirM system with other approaches. The simulations results show that the MLAirM can achieve a similar accuracy of building the global air quality map with a significant reduction in communication and sensing costs compared to other approaches.