Simulation environment for algorithms and agents evaluation.

  • Pablo Chamoso
    Catholic University of Daegu chamoso[at]
  • Fernando De La Prieta
    National University of Sunchon


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.
  • Referencias
  • Cómo citar
  • Del mismo autor
  • Métricas
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:
Chamoso, P., & De La Prieta, F. (2016). Simulation environment for algorithms and agents evaluation. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 4(3), 87–96.

Most read articles by the same author(s)

1 2 > >> 


Download data is not yet available.