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Observing the Pulse of a City: A Smart City Framework for Real-time Discovery, Federation, and Aggregation of Data Streams

Kolozali, Sefki and Bermudez-Edo, Maria and FarajiDavar, Nazli and Barnaghi, Payam and Gao, Feng and Intizar Ali, Muhammad and Mileo, Alessandra and Fischer, Martin and Iggena, Thorben and Kuemper, Daniel and Tonjes, Ralf (2018) 'Observing the Pulse of a City: A Smart City Framework for Real-time Discovery, Federation, and Aggregation of Data Streams.' IEEE Internet of Things Journal. ISSN 2327-4662

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Abstract

An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage and provide plans for public resources and infrastructures in city environments, while offering citizens and businesses to develop and use intelligent services in cities. However, providing such smart city applications gives rise to several issues such as semantic heterogeneity and trustworthiness of data sources, and extracting up-to-date information in real time from large-scale dynamic data streams. In order to address these issues, we propose a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city. The proposed framework enables efficient semantic integration of data streams and complex event processing on top of real-time data aggregation and quality analysis in a Semantic Web environment. To evaluate our system, we use real-time sensor observations that have been published via an open platform called Open Data Aarhus by the City of Aarhus. We examine the framework utilising Symbolic Aggregate Approximation to reduce the size of data streams, and perform quality analysis taking into account both single and multiple data streams. We also investigate the optimisation of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.

Item Type: Article
Uncontrolled Keywords: Smart Cities, Internet of Things, Time Series Analysis, Complex Event Processing, Quality Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 01 Oct 2018 14:21
Last Modified: 01 Oct 2018 15:15
URI: http://repository.essex.ac.uk/id/eprint/23170

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