A cost-effective approach to gather information in a smart city is to embed sensors in vehicles such as buses. To understand the limitations and opportunities of this model, it is fundamental to investigate the spatial coverage of such a network, especially in the case where only a subset of the buses have a sensing device embedded. In this paper, we propose a model to select the right subset of buses that maximizes the coverage of the city. We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among other findings, we observe that the fleet of buses covers approximately 5655 km of streets (approximately 47% of the streets) and show that it is possible to cover 94% of the same streets if only 18% of buses have sensing capabilities embedded.
Smart city applications need data about the city, and this data must follow specific requirements. Two of these requirements are the maximum delivery delay and the minimum measurement frequency. Using buses to gather data and bus stops as gateways can be cost-effective, but data might not fit the application requirements. In this thesis, we present a model to minimize the delay of data delivery, a metric to estimate the coverage, and a prototype of the nodes of such a network. We use GPS data from the bus fleet of Rio de Janeiro to show that it is possible to cover a significant part of the city, fulfilling application requirements specified by the smart city literature.
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