Abstract
Describing an accurate simulation model of the driving behavior of real-world vehicles is a laborious or even impossible task, because a driver reacts to a dynamically changing environment. As multiple external factors determine driving behavior, it is usually difficult to obtain an accurate model, owing to a lack of sensors or inability to collect data. In this paper, we propose a novel technique to combine driving behavior in vehicular Internet of Things (IoT) big data with a multi-agent system. This enables correct and scalable simulation without modeling the behavior of vehicular IoT devices or the environment. We develop an extensible simulation framework, called FlowSim, that demonstrates the application of our technique for a simulation of camera-image data collection from connected cars.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
It is desirable that one real vehicle is assigned to only one virtual vehicle, from the viewpoint of correctness; otherwise, this can duplicate the behavior of the real vehicle in the simulation, which is unrealistic. To this end, a sufficient number of real vehicles are required, namely \(|U| -|O| \ll |O|\) when \(|U| > |O|\).
- 2.
References
Apache Hadoop. https://hadoop.apache.org/
Apache Spark. https://spark.apache.org/
Automated Mapping Platform – HD Mapping that Empowers. https://www.tri-ad.global/areas-of-focus/automated-mapping-platform
CASE – Intuitive Mobility. https://www.daimler.com/innovation/case-2.html
GitHub (a software development platform). https://github.com/
Hazelcast IMDG (In-memory Data Grid). https://hazelcast.org/
Toyota to Display New Map Generation System at CES 2016. https://global.toyota/en/detail/10765074
Ahlbrecht, T., Dix, J., Fiekas, N.: Scalable multi-agent simulation based on MapReduce. In: Criado Pacheco, N., Carrascosa, C., Osman, N., Julián Inglada, V. (eds.) EUMAS/AT -2016. LNCS (LNAI), vol. 10207, pp. 364–371. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59294-7_31
Ahmed, B., Malik, A.W., Hafeez, T., Ahmed, N.: Services and simulation frameworks for vehicular cloud computing: a contemporary survey. EURASIP J. Wirel. Commun. Netw. 2019(1), 4 (2019)
Bham, G.H., Benekohal, R.F.: A high fidelity traffic simulation model based on cellular automata and car-following concepts. Trans. Res. Part C Emerg. Technol. 12(1), 1–32 (2004)
Blythe, J., et al.: Massive multi-agent data-driven simulations of the github ecosystem. In: Demazeau, Y., Matson, E., Corchado, J.M., De la Prieta, F. (eds.) Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection, pp. 3–15. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-24209-1_1
Burghout, W., Koutsopoulos, H.N., Andreasson, I.: A discrete-event mesoscopic traffic simulation model for hybrid traffic simulation. In: 2006 IEEE Intelligent Transportation Systems Conference, pp. 1102–1107. IEEE (2006)
Düntgen, C., Behr, T., Güting, R.H.: Berlinmod: a benchmark for moving object databases. VLDB J. 18(6), 1335 (2009)
Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London (2016)
Kohavi, R., Longbotham, R.: Online controlled experiments and a/b testing. Encycl. Mach. Learn. Data Min. 7(8), 922–929 (2017)
Lopez, P.A., et al.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582. IEEE (2018)
Padgham, L., Nagel, K., Singh, D., Chen, Q.: Integrating bdi agents into a matsim simulation. In: Proceedings of the Twenty-First European Conference on Artificial Intelligence, pp. 681–686. ECAI’14, IOS Press, NLD (2014)
Sommer, C., German, R., Dressler, F.: Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Trans. Mob. Comput. 10(1), 3–15 (2010)
Valiant, L.G.: A bridging model for parallel computation. Commun. ACM 33(8), 103–111 (1990)
Varga, A.: Discrete event simulation system. In: Proceedings of the European Simulation Multiconference (ESM’2001), pp. 1–7 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Neyama, R. et al. (2020). Simulating Vehicular IoT Applications by Combining a Multi-agent System and Big Data. In: Baroglio, C., Hubner, J.F., Winikoff, M. (eds) Engineering Multi-Agent Systems. EMAS 2020. Lecture Notes in Computer Science(), vol 12589. Springer, Cham. https://doi.org/10.1007/978-3-030-66534-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-66534-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66533-3
Online ISBN: 978-3-030-66534-0
eBook Packages: Computer ScienceComputer Science (R0)