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Convergent platform for multi-agent data processing in the “Smart Road” system

Alexey Germanovich Finogeev, Anton Finogeev, Irina Nefedova, Artur Lyapin


In the article, the multi-agent platform for convergent sensor data processing in a monitoring system for Smart Road Infrastructure are considered. The platform works with a network of spatially distributed photo-radar complexes, which in real time record road accidents. The paper discusses tools for collection of road accident’s photo and video data fixation, data mining and forecasting of transport incidents, depending on various factors (meteorological, social, operational, etc.). The results of monitoring and analysis of traffic accidents, fixed by an intelligent monitoring system with photo-radar complexes are considered. The connection between the complexes and the data processing center using a heterogeneous wireless network is established. A multi-agent approach developed to address the tasks of sensor data collecting and processing.  Convergent approach is the convergence of cloud, fog and mobile data processing technologies. The structure of the neural network is adapted to the diagnosing problems and forecasting. The tasks of intellectual analysis and forecasting traffic accidents solved. The hybrid fuzzy neural network synthesized. Because the comparison of time series of traffic accidents and time series of meteorological factors, it established that the presence of factors to become determinants for an abnormal change in the traffic situation in controlled areas. The monitoring system is a part of Smart Road Infrastructure within the framework of the Smart & Safe City concept.

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Batty, M., Axhausen, K.W., Giannotti, F., et al. Smart cities of the future. 2013. Available from: [Accessed: 2017-11-01]

Ouzounis, G., Portugali Y. Smart cities of the future. The European Physical Journal Special Topics. 2012. 214 (1): 481-518

Deakin, Mark; Al Waer, Husam. From Intelligent to Smart Cities. Taylor and Francis: 2012. 95p

Cook D., Das S. Smart Environments. Technologies, protocols and applications. — Hoboken NJ: Wiley-Interscience, 2005. 432p

Nakashima H., Aghajan H., Augusto J. C. Handbook of Ambient Intelligence and Smart Environments. New York: Springer. 2010. 413p

Hernandez-Muñoz, J. M. et al. Smart Cities at the Forefront of the Future Internet. In Proceedings of the Future Internet Assembly: Achievements and Technological Promises. Springer Berlin Heidelberg; 2011:447-462

Scott M. Kozel Roads to the Future Available from: [Accessed: 2017-11-01]

Nowacki G. Development and Standardization of Intelligent Transport Systems. Int. J. on Marine Navigation and Safety of Sea Transportation. 2012;6(3):403–411.

Dluha M. Praba Smart monitoring infrastructure on the smart road system: Available from: [Accessed: 2017-11-01]

Pandit A., Talreja J., Mundra A. RFID Tracking System for Vehicles (RTSV). In: Proceedings of the First International Conference on Computational Intelligence. Communication Systems and Networks. 2009. p.160-165

Esker Fritz. RFID in Vehicles. Louisville,Kentucky: NetWorld Alliance LLC. 2012. 143p.

Hong-JiaoMa, Yong-Hui Hu, Hai-Bo Yuan, Wei Guo. Design and Analysis of Embedded GPS/DR Vehicle Integrated Navigation System. In: Proceedings of the The 2008 International Conference on Embedded Software and Systems Symposia. 2008

Finogeev, A.G., Parygin D.S., Finogeev, A.A. et al. Multi-agent approach to distributed processing of big sensor data based on fog computing model for the monitoring of the urban infrastructure systems. In Proceedings of the 5th International Conference on System Modeling & Advancement in Research Trends. 2016;1: p. 305-310.

Finogeev, A.G., Parygin, D.S. & Finogeev, A.A. The convergence computing model for big sensor data mining and knowledge discovery. Human-centric Computing and Information Sciences. 2017;7:11. DOI:10.1186/s13673-017-0092-7.

Sadovnikova, N.P., Finogeev, A.G. Parygin, D.S et al. Monitoring of social reactions to support decision making on issues of urban territory management. In Proceedings of the 5th International Young Scientist Conference on Computational Science. 2017;101: 243-252.

Finogeev A., Finogeev A., Shevchenko S. Monitoring of Road Transport Infrastructure for the Intelligent Environment «Smart Road». In: Kravets A., Shcherbakov M., Kultsova M., Groumpos P. (eds) Creativity in Intelligent Technologies and Data Science. (CIT&DS 2017). Communications in Computer and Information Science, Springer, Cham. 2017;754: p.655-668.

K. Lorincz, D. Malan, T.R.F. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G. Mainland, M. Welsh, S. Moulton, Sensor networks for emergency response: challenges and opportunities, Pervasive Computing for First Response (Special Issue), IEEE Pervasive Computing, 2004. 3 (4):16-23.

Alberto Bielsa Smart Roads – Wireless Sensor Networks for Smart Infrastructures: A Billion Dollar Business Opportunity. 2013. Available from: [Accessed: 2017-11-01]

Roco M., Bainbridge W. (eds). Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Science. Arlington:Kluwer Academic Publisher, 2004:482p.

Niroshinie Fernando, Seng W. Loke, Wenny Rahayu Mobile cloud computing: A survey. In Proceedings of the Future Generation Computer Systems. 2013;29:1: p.84-106

Finogeev, A.G., Parygin, D.S., Finogeev A.A., et al. A convergent model for distributed processing of Big Sensor Data in urban engineering networks. Journal of Physics: Conference Series: In Proceedings of the International Conference on Information Technologies in Business and Industry. 2017;803: p.1-6.

Dargie, W. and Poellabauer, C. Fundamentals of wireless sensor networks: theory and practice, John Wiley and Sons, 2010

Stojmenovic I., Wen Sh. The Fog Computing Paradigm: Scenarios and Security Issues. In Proceedings of the Federated Conference on Computer Science and Information Systems (ACSIS). 2014;2: p. 1–8

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ser. MCC’12. ACM, 2012: p. 13–16

Jamal N. Al-Karaki, Raza Ul-Mustafa, Ahmed E. Kamal, "Data Aggregation in Wireless Sensor Networks - Exact and Approximate Algorithms'", In: Proceedings of IEEE Workshop on High Performance Switching and Routing (HPSR) IEEE. Phoenix, USA. 2004

M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” Commun. ACM, 2010; 53: 4: 50–58,

Lee B., Tim G., Patt-Corner R., Jeff V. Cloud Computing Synopsis and Recommendations. National Institute of Standards and Technology (US), Gaithersburg. NIST Special Publication 800-146. 2012:81p.

Google Cloud Platform. BigQuery. A fast, economical and fully managed data warehouse for large-scale data analytics. Available from: [Accessed: 2017-11-01]

Sadovnikova, N.P., Finogeev, A.G., Parygin D.S, Finogeev, A.A. et al. Visualization of data about events in the urban environment for the decision support of the city services actions coordination. In: Proceedings of the 5th International Conference on System Modeling & Advancement in Research Trends. 2016;1: 283-290.

Botvinkin PV, Kamaev VA, Nefedova IS, Finogeev AG (2015) On information of security risk management for GPS/GLONASS-based ground transportation monitoring and supervisory control automated navigation systems. The Social Sciences (Medwell Journals). 2015; 10 (2): 201-205.

Finogeev Alexey, Kamaev Valery, Fionova Ludmila, Finogeev Anton, Finogeev Egor, Mai Ngoc Thang Tools For Data Mining And Secure Transfer In The Wsn For Energy Management. Journal of Applied Engineering Research. 2015;10(15): 35373-35381.

Valery Kamaev, Alexey Finogeev, Anton Finogeev, Sergiy Shevchenko Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System. In: Proceeding of the Knowledge-Based Software Engineering (JCKBSE-14). 2014:1-15.

Finogeev, A.G., Skorobogatchenko, Dang Thanh Trung D.A., Kamaev, V.A. Application of indistinct neural networks for solving forecasting problems in the road complex. ARPN Journal of Engineering and Applied Sciences. 2016; 11(16): 9646 – 9653.

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