Modeling Real-Life Urban Sensor Networks Based on Open Data

Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and metropolitan areas. As the trend continues to expand, the need to efficiently monitor and manage smart city infrastructure, public transportation, service vehicles, and commercial fleets has become of higher importance. This, in turn, requires new methods for dissemination, collection, and processing of data from massive number of already deployed sensing devices. In order to transmit these data efficiently, it is necessary to optimize the connection structure in wireless networks. Emerging open access to real data from different types of networked and sensing devices should be leveraged. It enables construction of models based on frequently updated real data rather than synthetic models or test environments. Hence, the main objective of this article is to introduce the concept of network modeling based on publicly available geographic location data of heterogeneous nodes and to promote the use of real-life diverse open data sources as the basis of novel research related to urban sensor networks. The feasibility of designed modeling architecture is discussed and proved with numerous examples of modeled spatial and spatiotemporal graphs, which are essential in opportunistic routing-related studies using the methods which rely on graph theory. This approach has not been considered before in similar studies and in the literature.

Automated summary

Epidemics and pandemics impact global mobility trends, while an impending energy crisis will reshape transportation and energy use patterns, particularly in urban areas. This article introduces network modeling based on real-world data and advocates for using diverse open data sources for urban sensor network research.