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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. 

Smart city projects have the potential to improve the management of environmental and public infrastructure. However, the operational and capital expenditures of smart cities can prevent cities from becoming smarter. A notable factor that influences the cost is providing cellular Internet connectivity to IoT devices. 5G has been proposed as a possible solution, but projections show that 5G will not be able to support the load of billions of IoT devices coming online. To mitigate this, people, vehicles, and other nodes in transportation networks can be exploited to transmit non-urgent data by leveraging device-to-device communication in order to reduce cellular connectivity costs associated with smart city sensors. Hence, this paper addresses cost-effective edge node placement in smart cities that opportunistically leverage public transit networks. We introduce an algorithm that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithm is evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky and results show that our algorithm outperforms betweeness and in-degree centrality metrics with a reduction in latency of over 20 minutes.