Abstract
This article describes the possibility of using the ideas of FOG computing as an additional layer between robotic devices and the cloud infrastructure. FOG layer, represented as a P2P network in combination with the containerized cloud infrastructure inspired by microservice patterns, provides the ability to process data based on its time-sensitivity and to increase overall benefits despite the fact of exponential growth of data. We consider that the solution of assignment problem obtained in terms of the platform is one of the keys to achieve the goal of data analysis close to devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mung, C.: Fog networking: an overview on research opportunities. Princeton University (2015)
Fog Computing and the Internet of Things: Extend the cloud to where the things are. Cisco Whitepaper, 6 p (2015)
Francesco, P., et al.: Research on architecting microservices: trends, focus, and potential for industrial adoption. In: 2017 IEEE International Conference on Software Architecture (ICSA), pp. 21–30. IEEE (2017)
Bertsekas, D.P.: Auction algorithms for network flow problems. Comput. Optim. Appl. 1(1), 7–66 (1992)
Osipov, Yu.M.: Modern problems of innovation: the manual. TUSUR, Tomsk, 140 p (2012). https://edu.tusur.ru/publications/1056
Zaharia, M., et al.: Spark: cluster computing with working sets. HotCloud 2010, vol. 10, 95 p (2010)
Data Age 2025: The Evolution of Data to Life-Critical. An IDC White Paper sponsored by Seagate, 25 p. April 2017
Zavlanos, M.M., Spesivtsev L., Pappas G.J.: A distributed auction algorithm for the assignment problem. In: 47th IEEE Conference on Decision and Control, CDC 2008, pp. 1212–1217 (2008)
Olfati-Saber, R., Murray, R.M.: Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49(9), 1520–1533 (2004)
Smith, S.L., Bullo, F.: Target assignment for robotic networks: worst case and stochastic performance in dense environments. In: Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, pp. 3585–3590 (2007)
Balinski, M.L.: Signature methods for the assignment problem. J. Oper. Res. 33, 527–537 (1985)
Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. 2(1–2), 83–97 (1955)
Bertsekas, D.P., Castanon, D.A.: Parallel synchronous and asynchronous implementations of the auction algorithm. Parallel Comput. 17, 707–732 (1991)
Kostyuchenko, E., Gurakov, M., Krivonosov, E., Tomyshev, M., Mescheryakov, R., Hodashinskiy, I.: Integration of Bayesian classifier and perceptron for problem identification on dynamics signature using a genetic algorithm for the identification threshold selection. In: Cheng, L., Liu, Q., Ronzhin, A. (eds.) ISNN 2016. LNCS, vol. 9719, pp. 620–627. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40663-3_71
Khodashinsky, I.A., Meshcheryakov, R.V., Anfilofiev, A.E.: Identification of fuzzy classifiers based on weed optimization algorithm. In: Kravets, A., Shcherbakov, M., Kultsova, M., Shabalina, O. (eds.) CIT&DS 2015. CCIS, vol. 535, pp. 216–223. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23766-4_18
Khodashinsky, I.A., Zemtsov, N.N., Meshcheryakov, R.V.: Construction of fuzzy approximators based on the bacterial foraging method. Russ. Phys. J. 55(3), 301–305 (2012)
Osipov, O.Yu., Osipov, Yu.M., Meshcheryakov, R.V.: Active driveline as an element of cyberphysical system. In: Proceedings of the Higher Educational Institutions (2016). Instrument 59(11), 934–938
Vasiliev, A.V., Kondratyev, A.S., Gradovtsev, A.A., Dalyaev, IYu.: Research and development of design shape of a mobile robotic system for geological exploration on the moon’s surface. SPIIRAS Proc. 45(2), 141–156 (2016). https://doi.org/10.15622/sp.45.9
Kryuchkov, B.I., Karpov, A.A., Usov, V.M.: Promising approaches for the use of service robots in the domain of manned space exploration. SPIIRAS Proc. 32(1), 125–151 (2014). https://doi.org/10.15622/sp.32.9
Motienko, A.I., Tarasov, A.G., Dorozhko, I.V., Basov, O.O.: Proactive control of robotic systems for rescue operations. SPIIRAS Proc. 46(3), 169–189 (2016). https://doi.org/10.15622/sp.46.12
Ronzhin, A., Saveliev, A., Basov, O., Solyonyj, S.: Conceptual model of cyberphysical environment based on collaborative work of distributed means and mobile robots. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2016. LNCS (LNAI), vol. 9812, pp. 32–39. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43955-6_5
Acknowledgements
The given paper is completed with the support of the Ministry of Education and Science of the Russian Federation within the limits of the project part of the state assignment of TUSUR in 2017 and 2019 (project 2.3583.2017) and science school (â„– NSH-3070.2018.8).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chueshev, A., Melekhova, O., Meshcheryakov, R. (2018). Cloud Robotic Platform on Basis of Fog Computing Approach. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2018. Lecture Notes in Computer Science(), vol 11097. Springer, Cham. https://doi.org/10.1007/978-3-319-99582-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-99582-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99581-6
Online ISBN: 978-3-319-99582-3
eBook Packages: Computer ScienceComputer Science (R0)