  Smart city projects aim to enhance the management of city infrastructure by
enabling government entities to monitor, control and maintain infrastructure
efficiently through the deployment of Internet-of-things (IoT) devices.
However, the financial burden associated with smart city projects is a
detriment to prospective smart cities. A noteworthy factor that impacts the
cost and sustainability of smart city projects is providing cellular Internet
connectivity to IoT devices. In response to this problem, this paper explores
the use of public transportation network nodes and mules, such as bus-stops as
buses, to facilitate connectivity via device-to-device communication in order
to reduce cellular connectivity costs within a smart city. The data mules
convey non-urgent data from IoT devices to edge computing hardware, where data
can be processed or sent to the cloud. Consequently, this paper focuses on edge
node placement in smart cities that opportunistically leverage public transit
networks for reducing reliance on and thus costs of cellular connectivity. We
introduce an algorithm that selects a set of edge nodes that provides maximal
sensor coverage and explore another that selects a set of edge nodes that
provide minimal delivery delay within a budget. The algorithms are evaluated
for two public transit network data-sets:Chapel Hill, North Carolina and
Louisville, Kentucky. Results show that our algorithms consistently outperform
edge node placement strategies that rely on traditional centrality
metrics(betweenness and in-degree centrality) by over 77% reduction in coverage
budget and over 20 minutes reduction in latency.
