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Smart city article details
| Title | Modulo: Drive-By Sensing At City-Scale On The Cheap |
|---|---|
| ID_Doc | 37805 |
| Authors | Agarwal D.; Iyengar S.; Swaminathan M.; Sharma E.; Raj A.; Hatwar A. |
| Year | 2020 |
| Published | COMPASS 2020 - Proceedings of the 2020 3rd ACM SIGCAS Conference on Computing and Sustainable Societies |
| DOI | http://dx.doi.org/10.1145/3378393.3402275 |
| Abstract | Ambient air pollution in urban areas is a significant health hazard, with over 4.2 million deaths annually attributed to it. A crucial step in tackling these challenge is to measure air quality at a fine spatiotemporal granularity. A promising approach for several smart city projects, called drive-by sensing, is to leverage vehicles retrofitted with different sensors (pollution monitors, etc.) that can provide the desired spatiotemporal coverage at a fraction of the cost. However, deploying a drive-by sensing network at a city-scale to optimally select vehicles from a large fleet is still unexplored. In this paper, we propose Modulo - a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints. Modulo is well-suited to satisfy unique deployment constraints such as colocations with other sensors (needed for gas and PM sensor calibration), etc. We compare Modulo with two baseline algorithms on real-world taxi and bus datasets. Modulo significantly outperforms the baselines when a fleet comprises of both taxis and fixed-route vehicles such as public transport buses. Finally, we present a real-world case study that uses Modulo to select vehicles for an air pollution sensing application. © 2020 ACM. |
| Author Keywords | drive-by sensing; low-cost sensing; optimal sensor deployment |
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