Drive-by sensing has emerged as a popular way to achieve fine-grained sensing of physical phenomena. However, for it to be effective at a city-scale, there is a need to optimally select a subset of vehicles from a larger available fleet. These chosen vehicles must maximize coverage of the entire city. Simultaneously, they must fulfill other deployment requirements specific to the sensing application such as reference-monitor colocation instances for gas sensors. In this paper, we describe a system to evaluate the coverage offered by different subsets of vehicles for sensor deployment based on historical vehicle mobility data. Our system allows evaluation of different vehicle selection algorithms, and also provides two in-built baselines — i) Random-MP, and ii) MaxPoints — for comparison. Finally, we provide visualizations showing coverage to gauge the efficacy of different vehicle selections.<\/p>\n","protected":false},"excerpt":{"rendered":"
Drive-by sensing has emerged as a popular way to achieve fine-grained sensing of physical phenomena. However, for it to be effective at a city-scale, there is a need to optimally select a subset of vehicles from a larger available fleet. These chosen vehicles must maximize coverage of the entire city. Simultaneously, they must fulfill other 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