Swarm-Based Smart City Platform: A Traffic Application

Pablo CHAMOSO, Fernando DE LA PRIETA

Abstract


Smart cities are proposed as a medium-term option for all cities. This article aims to propose an architecture that allows cities to provide solutions to interconnect all their elements. The study case focuses in locating and optimized regulation of traffic in cities. However, thanks to the proposed structure and the applied algorithms, the architecture is scalable in size of the sensor network, in functionality or even in the use of resources. A simulation environment that is able to show the operation of the architecture in the same way that a real city would, is presented.


Keywords


Agents; Swarm intelligence; Locating Systems; Smart cities

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References


Nam, T., & Pardo, T. A. (2011, June). Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times (pp. 282-291). ACM. http://dx.doi.org/10.1145/2037556.2037602

Renuka, N., Nan, N. C., & Ismail, W. (2013, September). Embedded RFID tracking system for hospital appli-cation using WSN platform. In RFID-Technologies and Applications (RFID-TA), 2013 IEEE International Conference on (pp. 1-5). IEEE.

Daniel, F., Eriksson, J., Finne, N., Fuchs, H., Gaglione, A., Karnouskos, S., ... & Voigt, T. (2013, April). makeSense: Real-world Business Processes through Wireless Sensor Networks. In CONET/UBICITEC (pp. 58-72).

Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.

Wooldridge, M. J. (2000). Reasoning about rational agents. MIT press.

Rao, A. S., & Georgeff, M. P. (1991). Modeling rational agents within a BDI-architecture. KR, 91, 473-484.

Bin, W., & Zhongzhi, S. (2001). A clustering algorithm based on swarm intelligence. In Info-tech and Info-net, 2001. Proceedings. ICII 2001-Beijing. 2001 International Conferences on (Vol. 3, pp. 58-66). IEEE. http://dx.doi.org/10.1109/ICII.2001.983036

Marciniak, A., Kowal, M., Filipczuk, P., & Korbicz, J. (2014). Swarm Intelligence Algorithms for Multi-level Image Thresholding. In Intelligent Systems in Technical and Medical Diagnostics (pp. 301-311). Springer Ber-lin Heidelberg. http://dx.doi.org/10.1007/978-3-642-39881-0_25

Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82(1), 1-42. http://dx.doi.org/10.1007/s10994-010-5216-5

Chamoso, P., Perez, A., Rodriguez, S., Corchado, J. M., Sempere, M., Rizo, R., ... & Pujol, M. (2014, July). Modeling Oil-Spill Detection with multirotor systems based on multi-agent systems. In Information Fusion (FUSION), 2014 17th International Conference on (pp. 1-8). IEEE.

Tatomir, B., & Rothkrantz, L. J. M. (2005). H-ABC: A scalable dynamic routing algorithm. Recent Advances in Artificial Life, 5, 8. ISO 690. http://dx.doi.org/10.1142/9789812701497_0021

Tatomir, B., & Rothkrantz, L. (2006, September). Hierarchical routing in traffic using swarm-intelligence. In Intelligent Transportation Systems Conference, 2006. ITSC'06. IEEE (pp. 230-235). IEEE. http://dx.doi.org/10.1109/itsc.2006.1706747

Wagner, M. (19/03/2015) Car System Digital Forum. Germany.Gebr. FALLER GmbH. https://www.car-system-digital.de/de/




DOI: http://dx.doi.org/10.14201/ADCAIJ2015428998





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