Towards Interoperability of Smart City Data Platforms

Abstract

We present a comprehensive analysis of the literature on interoperability of smart city data platforms in an attempt to conceptualize interoperability approaches. To this end, we propose a taxonomy of said approaches based on four dimensions with three characteristics each. The taxonomy can be used to classify interoperability approaches. We discuss implications for theory and practice and conclude with a first assessment of individual approaches towards their prospect of success.

A Survey of Interoperability Challenges and Solutions for Dealing With Them in IoT Environment

“…• Interoperability is the process of transmitting data in one context and receiving and interpreting it in another [64]. • Interoperability is the intermediary of connecting different platforms and creating a comprehensive database that contributes to the development of new services, reducing their costs, and providing open markets for working on data [34]. • Interoperability is a way of creating a collaborative environment between developers that enables them to create widely acceptable systems [60].…”

“…So far, smart cities use closed systems designed for limited tasks, which cannot be integrated or extended, as there are restrictions on data exchange and accessibility [34]. Also, different areas of smart cities create additional difficulty, because they generate different and huge [35] data and need standardization and real-time processing with [36] standardized query method.…”

“…Application Programming Interfaces (APIs) enable every system or application that provides public methods which can be called from other applications and systems so that these functions provide some services or data in the form required for the caller [31]. An API is usually suitable for agreeing on specific and common focal points only, and a unified standard must be relied on [34]. In [70], the necessity and importance of enabling easy data collection and sharing by providing open APIs are emphasized.…”

Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

“…SLR Scope/Focus [4] "A systematic literature review on maturity models that assess the level of maturity for smart city projects" [11] "A systematic literature survey on software architectures for big data systems", with a few connections with SC [12] "IoT challenges in smart cities and provide the gap between the existing state-of-the-art IoT application on S" [13] "A comprehensive analysis of the literature on interoperability of SC data platforms" [10] "Analyze the link between the concepts of smart cities, machine learning techniques and their applicability" [14] "Systematically investigated the evolution of OGD research" [15] "the relationship between big and open data and how they relate to the broad concept of open government" [16] "Covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities" and concludes that "open data movement has increased the number of research works in the field of machine learning, especially in the prediction of air quality". [17] "Comprehensive survey that explores the application of graph neural networks for traffic forecasting problems", presenting also "a comprehensive list of open data and source resources for each problem" [18] "The challenges faced by smart cities and the key role data mining, machine learning and statistical methods can play to enable intelligent solutions for different applications" [19] "Systematically reviews the top 200 Google Scholar publications in the area of smart city with the aid of data-driven methods from the fields natural language processing and time series forecasting" [7] "Generates insights into how AI can contribute to the development of smarter cities".…”

“…During the last decade, the concept of SC has evolved from the simple implementation of information technologies in the public services into an ecosystem that also takes into account innovation, the environment, the human, or the social aspects [4]. In [13], SC is considered not an ecosystem, but a development vision where ITC based solutions are integrated, data is acquired from heterogeneous sources, and assets are connected in a platform with the objective of improving life's quality while enhancing the efficiency and the economical value.…”