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Enhancing drive-by sensing power in urban hotspots through multi-objective vehicle selection optimization

Year: 2025
Type: article
Abstract: Drive-by sensing, using vehicles as mobile sensors to collect environmental data, offers high spatiotemporal resolution monitoring. In drive-by sensing tasks, urban areas with intense human activity or pollution – termed high-priority hotspots – demand frequent sensing for adequate data collection. However, prior studies rarely address optimizing vehicle selection to enhance hotspot coverage frequency while maintaining non-hotspot coverage. This study formalized this problem as the Maximal Hotspot Regular Coverage Problem and proposed an Adaptive Level-Aware Vehicle Selection algorithm. Using air pollution hotspot sensing in Beijing as an empirical case, results show the advantage of the proposed algorithm over other baselines: Random selection (RS), Non-hotspot Greedy Adding and Multi-type hotspot Greedy MaxMin, covering 29.99% of hotspots and 77.08% non-hotspot zones with 1000 sensors on average. The spatial distribution of covered area and statistical distribution of average visits per hour reveals a large spatial range and high revisit times of the method, and its advantage over baselines in a hybrid sensing scenario is also proven (with 54.18% daily average coverage among all zones and 31.22% on hotspots). This study provides a method to advance the coverage of hotspots in single-type and hybrid sensing scenarios, inspiring a future extension of optimization based on the proposed algorithm.
Source: Figshare
Authors: Hou, Yuanqiao, Chen, Xiaojian, Dong, Quanhua, Zhang, Fan, Sun, Yumei, Wu, Lun, Liu, Yu
Language: English
Open Access status: green
OpenAlex ID: W6921000304
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