@inproceedings{agarwal2020modulo,
author = {Agarwal, Dhruv and Iyengar, Srinivasan and Manohar, Swami and Sharma, Eash and Raj, Ashish and Hatwar, Aadithya},
title = {Modulo: Drive-by Sensing at City-scale on the Cheap},
organization = {ACM},
booktitle = {COMPASS 2020},
year = {2020},
month = {June},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/modulo-drive-by-sensing-at-city-scale-on-the-cheap/},
}