Smart Cities Simulation Environment for Intelligent Algorithms Evaluation

  • Pablo ChamosoEmail author
  • Juan F. De Paz
  • Sara Rodríguez
  • Javier Bajo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 476)


This paper 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.


Smart Cities Intelligent systems Machine to Machine Internet of Services Big data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Calvillo, C.F., Sánchez-Miralles, A., Villar, J.: Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews 55, 273–287 (2016)CrossRefGoogle Scholar
  2. 2.
    Chamoso, P., De la Prieta, F., De Paz, F., Corchado, J.M.: Swarm agent-based architecture suitable for internet of things and smartcities. In: 12th International Conference on Distributed Computing and Artificial Intelligence, pp. 21–29. Springer International Publishing (2015)Google Scholar
  3. 3.
    Chamoso, P., De la Prieta, F., Bajo, J., Corchado, J.M.: Conflict Resolution with Agents in Smart Cities. Interdisciplinary Perspectives on Contemporary Conflict Resolution. IGI Global (2016)Google Scholar
  4. 4.
    Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., Scholl, H.J.: Understanding smart cities: an integrative framework. In: 2012 45th Hawaii International Conference on System Sciences, pp. 2289–2297 (2012)Google Scholar
  5. 5.
    Gómez-Romero, J., Serrano, M.A., Patricio, M.A., García, J., Molina, J.M.: Context-based scene recognition from visual data in smart homes: an information fusion approach. Personal and Ubiquitous Computing 16(7), 835–857 (2012)CrossRefGoogle Scholar
  6. 6.
    Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., Morris, R.: Smarter cities and their innovation challenges. Computer 44(6), 32–39 (2011)CrossRefGoogle Scholar
  7. 7.
    Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N., Nelson, L.E.: Helping CIOs Understand “Smart City” Initiatives. Growth 17 (2009).

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pablo Chamoso
    • 1
    Email author
  • Juan F. De Paz
    • 1
  • Sara Rodríguez
    • 1
  • Javier Bajo
    • 2
  1. 1.Computer and Automation DepartmentUniversity of SalamancaSalamancaSpain
  2. 2.Artificial Intelligence DepartmentPolytechnic University of MadridMadridSpain

Personalised recommendations