  Drive-by sensing is gaining popularity as an inexpensive way to perform
fine-grained, city-scale, spatiotemporal monitoring of physical phenomena.
Prior work explores several challenges in the design of low-cost sensors, the
reliability of these sensors, and their application for specific use-cases like
pothole detection and pollution monitoring. However, the process of deployment
of a drive-by sensing network at a city-scale is still unexplored. Despite the
rise of ride-sharing services, there is still no way to optimally select
vehicles from a fleet that can accomplish the sensing task by providing enough
coverage of the city. 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. Further, 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. We
find that Modulo marginally outperforms the two baselines for datasets with
just random-routes vehicles such as taxis. However, it 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-deployment
that uses Modulo to select vehicles for an air pollution sensing application.
