详细信息
Advancing population-targeted urban sensing: A comparative study on mobile and static sensing paradigms ( EI收录)
文献类型:期刊文献
英文题名:Advancing population-targeted urban sensing: A comparative study on mobile and static sensing paradigms
作者:Hou, Yuan-Qiao[1];Chen, Xiao-Jian[1];Huang, Zhou[1];Peng, Xia[2];Liu, Yu[1]
第一作者:Hou, Yuan-Qiao
通讯作者:Chen, XJ[1]
机构:[1]Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China;[2]Beijing Union Univ, Tourism Coll, Beijing, Peoples R China
第一机构:Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
通讯机构:[1]corresponding author), Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China.
年份:2025
卷号:119
外文期刊名:COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
收录:;EI(收录号:20251518195422);Scopus(收录号:2-s2.0-105001959933);WOS:【SSCI(收录号:WOS:001466677800001)】;
基金:This research was supported by grants from the National Natural Science Foundation of China (Grant No. 42430106, 42201507, U2344216, 42271471)
语种:英文
外文关键词:Urban environmental monitoring; Population-targeted sensing power; Static sites; Taxi-based mobile sensing
摘要:To evaluate human exposure to environmental factors, sufficient population-targeted sensing power of sensor carriers is crucial. However, the traditional static sensing approach is constrained by its limited coverage. Recently, equipping moving vehicles with sensors has emerged as a new approach. However, a quantitative comparison between mobile and traditional static sensing is still lacking. Using empirical taxi trajectory and population data in Beijing and Xiamen, we found that while a small number of taxi-based mobile sensors can cover a larger portion of the population, well-located static sensors eventually surpass mobile sensors in coverage as their number increases. In addition, a higher required frequency reduces the coverage of mobile sensors, whereas a higher cost ratio between static and mobile sensors reduces the coverage of static sites. Taxis provide extensive spatial coverage but with some uncertainty, especially in peripheral areas, whereas static sensors ensure localized and stable coverage. Based on the advantage of taxis and static sites, we propose an effective greedy-add-guided strengthen elitist genetic algorithm to determine the optimal combination of static and mobile sensors. The key idea is to position static sensors in areas with relatively low taxi visit probabilities but high population density. The results indicate that this optimal combination achieves higher population coverage compared to using taxis alone. It addresses the uncertainty in taxi coverage and significantly reduces the number of sensors required. These results support the feasibility of using taxis as a sensing paradigm and further highlight the potential of combining these two sensing paradigms in population-targeted sensing applications.
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