Intelligent coverage and cost-effective monitoring: Bus-based mobile sensing for city air quality

Abstract

Bus-based mobile sensing has emerged as a cost-effective approach for collecting high spatio-temporal air quality data by leveraging the mobility of buses. However, when selecting an optimal subset of buses from a large fleet for deploying a limited number of sensors, existing studies have primarily focused on assessing the coverage of the study area by buses, disregarding the temporal gap between consecutive coverage at specific locations. It is worth noting that pollutant concentrations exhibit smooth variations over time, rendering data collected at very short intervals redundant. Therefore, this study first identified five key criteria for evaluating the air quality monitoring importance in various locations. Then two bus selection models that consider both the spatiotemporal coverage of the study area and the temporal gap between sensing data are proposed. Specifically, the maximal spatio-temporal coverage bus selection model (MaxCoverage) maximizes overall spatio-temporal coverage with a guaranteed time interval between consecutive sensor measurements, and the minimal fleet size model (MiniSize) selects the minimum number of buses based on based on specified requirements for monitoring time interval and counts. Experimental validation using a real-world bus trajectory dataset from Shenzhen, China demonstrates the effectiveness of the proposed models. The results show that the MaxCoverage_TC1 model has time intervals 2.7 timeslots longer than the baseline, and the MiniSize_TC1 model has an average time interval that is 1.4 timeslots longer.