Component-based modelling for scalable smart city systems interoperability: A case study on integrating energy demand response systems

Journal article


Palomar, E, Chen, X, Liu, Z, Maharjan, S and Bowen, J. (2016). Component-based modelling for scalable smart city systems interoperability: A case study on integrating energy demand response systems. Sensors. 16 (11), p. 1810.
AuthorsPalomar, E, Chen, X, Liu, Z, Maharjan, S and Bowen, J.
Abstract

© 2016 by the authors; licensee MDPI, Basel, Switzerland. Smart city systems embrace major challenges associated with climate change, energy efficiency, mobi lity and future services by embedding the virtual space into a complex cyber-physical system. Those systems are constantly evolving and scaling up, involving a wide range of integration among users, devices, utilities, public services and also policies. Modelling such complex dynamic systems’ architectures has always been essential for the development and application of techniques/tools to support design and deployment of integration of new components, as well as for the analysis, verification, simulation and testing to ensure trustworthiness. This article reports on the definition and implementation of a scalable component-based architecture that supports a cooperative energy demand response (DR) system coordinating energy usage between neighbouring households. The proposed architecture, called refinement of Cyber-Physical Component Systems (rCPCS), which extends the refinement calculus for component and object system (rCOS) modelling method, is implemented using Eclipse Extensible Coordination Tools (ECT), i.e., Reo coordination language. With rCPCS implementation in Reo, we specify the communication, synchronisation and co-operation amongst the heterogeneous components of the system assuring, by design scalability and the interoperability, correctness of component cooperation.

Keywordscomputer science; Internet of Things; component system interoperability and coordination; component-based architecture design; cooperative demand response; scalable modelling; smart city system modelling; 0301 Analytical Chemistry; 0906 Electrical And Electronic Engineering; Analytical Chemistry
Year2016
JournalSensors
Journal citation16 (11), p. 1810
PublisherLondon South Bank University
ISSN1424-8220
Digital Object Identifier (DOI)doi:10.3390/s16111810
Publication dates
Print01 Nov 2016
Publication process dates
Deposited17 May 2018
Accepted01 Nov 2016
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/871vx

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