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Automated Discovery and Integration of Urban Data Streams: the ACEIS Middleware

Publication Type: 
Refereed Original Article
Abstract: 
With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards smart cities solutions that can leverage this rich source of streaming data to gather knowledge that can be used to solve domain-specific problems. A key challenge that needs to be faced in this respect is the ability to automatically discover and integrate heterogeneous sensor data streams on the fly for applications to use them. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event Implementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS not only automatically discovers and composes IoT streams in urban infrastructures for users’ requirements expressed as complex event requests, but also automatically generates stream queries in order to detect the requested complex events, bridging the gap between high-level application users and low-level information sources. We also demonstrate the use of ACEIS in a smart travel planner scenario using real-world sensor devices and datasets
Digital Object Identifer (DOI): 
10.1016/j.future.2017.03.002
Publication Status: 
In Press
Date Accepted for Publication: 
Wednesday, 22 March, 2017
Publication Date: 
30/03/2017
Journal: 
Future Generation Computer Systems
Research Group: 
Institution: 
DCU
Open access repository: 
Yes