Abstract
The ever-growing amount of data produced by and in today’s smart cities offers significant potential for novel applications created by city stakeholders as well as third parties. Current smart city application models mostly assume that data is exclusively managed by and bound to its original application and location. We argue that smart city data must not be constrained to such data silos so that future smart city applications can seamlessly access and integrate data from multiple sources across multiple cities. In this paper, we present a methodology and toolset to model available smart city data sources and enable efficient, distributed data access in smart city environments. We introduce a modeling abstraction to describe the structure and relevant properties, such as security and compliance constraints, of smart city data sources along with independently accessible subsets in a technology-agnostic way. Based on this abstraction, we present a middleware toolset for efficient and seamless data access through autonomous relocation of relevant subsets of available data sources to improve Quality of Service for smart city applications based on a configurable mechanism. We evaluate our approach using a case study in the context of a distributed city infrastructure decision support system and show that selective relocation of data subsets can significantly reduce application response times.
Highlights
Sparked by the rapid adoption of the smart city paradigm and fueled by the rise of the Internet of Things, today’s metropolises have become data behemoths
As basis for our evaluation we used the URBEM Smart City Application (USCA) (Schleicher et al, 2016c), a holistic interdisciplinary decision support system, which has been used for city infrastructure planning tasks especially in the context of energy and mobility systems
We choose the USCA, because it represents an optimal candidate for our evaluation due to the following characteristics: (i) It heavily relies on a diverse set of data sources, where most of them belong to stakeholders and are under strict security and compliance regulations; (ii) It is an application that has to deal with changing requirements that make it necessary to incorporate new data sources dynamically; (iii) Due to the nature of the application as a planning tool for energy and mobility systems it is a common case to incorporate data sources from other cities around the globe
Summary
Sparked by the rapid adoption of the smart city paradigm and fueled by the rise of the Internet of Things, today’s metropolises have become data behemoths. This data is mostly isolated and restricted to certain application areas, data centers, organizations, or only accessible in a specific city. We introduce a system model that provides a simple abstraction for the technology-agnostic description of data sources and their subsets with the ability to express varying data granularities and specific characteristics common in the smart city domain Based on this abstraction, we present the SDD framework, a middleware toolset that enables efficient and seamless data access for smart city applications by autonomously relocating relevant subsets of available data sources to improve Quality of Service (QoS) based on a configurable mechanism that considers request latency, as well as costs for data transfer, storage, and updates. Related work is discussed in ‘Related Work’, followed by a conclusion and outlook on future research in ‘Conclusion’
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