Main Article Content

Pablo Chamoso
Catholic University of Daegu
Korea, Republic of
Fernando De La Prieta
National University of Sunchon
Korea, Republic of
Vol. 4 No. 3 (2015), Articles, pages 87-96
DOI: https://doi.org/10.14201/ADCAIJ2015438796
Accepted: Jun 22, 2016
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Abstract

This article presents an adaptive platform that can simulate the centralized control of different smart city areas. For example, public lighting and intelligent management, public zones of buildings, energy distribution, etc. It can operate the hardware infrastructure and perform optimization both in energy consumption and economic control from a modular architecture which is fully adaptable to most cities. Machine-to-machine (M2M) permits connecting all the sensors of the city so that they provide the platform with a perfect perspective of the global city status. To carry out this optimization, the platform offers the developers a software that operates on the hardware infrastructure and merges various techniques of artificial intelligence (AI) and statistics, such as artificial neural networks (ANN), multi-agent systems (MAS) or a Service Oriented Approach (SOA), forming an Internet of Services (IoS). Different case studies were tested by using the presented platform, and further development is still underway with additional case studies.

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References

Calvillo, C. F., Sánchez-Miralles, A., & Villar, J. (2016). Energy management and planning in smart cities. Re-newable and Sustainable Energy Reviews, 55, 273-287.

Chamoso, P., De la Prieta, F., De Paz, F., & Corchado, J. M. (2015). Swarm Agent-Based Architecture Suitable for Internet of Things and Smartcities. In Distributed Computing and Artificial Intelligence, 12th Interna-tional Conference (pp. 21-29). Springer International Publishing.

Chamoso, P., De la Prieta, F., Bajo, J., & Corchado, J. M. (2016). Conflict Resolution with Agents in Smart Ci-ties. Interdisciplinary Perspectives on Contemporary Conflict Resolution. IGI Global.

Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., and Scholl, H. J. (2012). Understanding Smart Cities: An Integrative Framework. 2012 45th Hawaii International Conference on System Sciences, 2289-2297.

Gómez-Romero, J., Serrano, M. A., Patricio, M. A., García, J., & Molina, J. M. (2012). Context-based scene recognition from visual data in smart homes: an information fusion approach. Personal and Ubiquitous Computing, 16(7), 835-857.

Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., & Morris, R. (2011). Smarter cities and their innovation challenges. Computer, 44(6), 32-39.

Washburn, D., Sindhu, U., Balaouras, S., Dines, R. a, Hayes, N., and Nelson, L. E. (2009). Helping CIOs Un-derstand "Smart City" Initiatives. Growth, 17. Available at: http://c3328005.r5.cf0.rackcdn.com/73efa931-0fac-4e28-ae77-8e58ebf74aa6.pdf.