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

Kolozali, Sefki, Bermudez-Edo, Maria, FarajiDavar, Nazli, Barnaghi, Payam, Gao, Feng, Intizar Ali, Muhammad, Mileo, Alessandra, Fischer, Marten, Iggena, Thorben, Kuemper, Daniel et al 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.

Preview

Text __homes.surrey.ac.uk_home_.System_Desktop_bare_jrnlFinal.pdf - Accepted version Manuscript Download (2MB) | Preview

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
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Kolozali, Sefki s.kolozali@surrey.ac.uk Bermudez-Edo, Maria FarajiDavar, Nazli Barnaghi, Payam P.Barnaghi@surrey.ac.uk Gao, Feng Intizar Ali, Muhammad Mileo, Alessandra Fischer, Marten Iggena, Thorben Kuemper, Daniel Tonjes, Ralf
Date :	28 September 2018
Funders :	FP7; Horizon 2020
DOI :	10.1109/JIOT.2018.2872606
Copyright Disclaimer :	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords :	Smart Cities, Internet of Things, Time Series Analysis, Complex Event Processing, Quality Analysis
Depositing User :	Melanie Hughes
Date Deposited :	02 Oct 2018 13:33
Last Modified :	04 Feb 2019 10:24
URI:	http://epubs.surrey.ac.uk/id/eprint/849487

Actions (login required)

View Item

Downloads