Smart cities simulation environment for intelligent algorithms evaluation

  1. CHAMOSO, Pablo 1
  2. DE LA PRIETA, Fernando 2
  1. 1 Catholic University of Daegu
    info

    Catholic University of Daegu

    Gyeongsan-si, Corea del Sur

    ROR https://ror.org/04fxknd68

  2. 2 National University of Sunchon
Zeitschrift:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Datum der Publikation: 2015

Ausgabe: 4

Nummer: 3

Seiten: 87-96

Art: Artikel

DOI: 10.14201/ADCAIJ2015438796 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Zusammenfassung

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.

Bibliographische Referenzen

  • 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.
Fuente de los datos: Dialnet