Research Article
A scalable middleware-based infrastructure for energy management and visualization in city districts
@ARTICLE{10.4108/eai.28-6-2017.152753, author={Francesco G. Brundu and Edoardo Patti and Matteo Del Giudice and Anna Osello and Michela Ramassotto and Fabrizio Massara and Francesca Marchi and Alberto Musetti and Enrico Macii and Andrea Acquaviva}, title={A scalable middleware-based infrastructure for energy management and visualization in city districts}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={3}, number={9}, publisher={EAI}, journal_a={CS}, year={2017}, month={6}, keywords={Smart City, Pervasive Computing, Smart Devices, Internet of Things, Middleware, District}, doi={10.4108/eai.28-6-2017.152753} }- Francesco G. Brundu
Edoardo Patti
Matteo Del Giudice
Anna Osello
Michela Ramassotto
Fabrizio Massara
Francesca Marchi
Alberto Musetti
Enrico Macii
Andrea Acquaviva
Year: 2017
A scalable middleware-based infrastructure for energy management and visualization in city districts
CS
EAI
DOI: 10.4108/eai.28-6-2017.152753
Abstract
Following the Smart City views, citizens, policy makers and energy distribution companies need a reliable and scalable infrastructure to manage and analyse energy consumption data in a city district context. In order to move forward this view, a city district model is needed, which takes into account di erent data-sources such as Building Information Models, Geographic Information Systems and real-time information coming from heterogeneous devices in the district. The Internet of Things paradigm is creating new business opportunities for low-cost, low-power and high-performance devices. Nevertheless, because of the "smart devices" heterogeneity, in order to provide uniform access to their functionalities, an abstract point of view is needed. Therefore, we propose an distributed software infrastructure, exploiting service-oriented middleware and ontology solutions to cope with the management, simulation and visualization of district energy data.
Copyright © 2017 Brundu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
