Abstract
Electric Autonomous Vehicles (EAVs) have gained increasing attention of industry, governments and scientific communities concerned about issues related to classic transportation including accidents and casualties, gas emissions and air pollution, intensive traffic and city viability. One of the aspects, however, that prevent a broader adoption of this technology is the need for human interference to charge EAVs, which is still mostly manual and time-consuming. This study approaches such a problem by introducing the Inno-EAV, an open-source charging framework for EAVs that employs machine-to-machine (M2M) distributed communication. The idea behind M2M is to have networked devices that can interact, exchange information and perform actions without any manual assistance of humans. The advantages of the Inno-EAV include the automation of charging processes and the collection of relevant data that can support better decision making in the spheres of energy distribution. In this paper, we present the software design of the framework, the development process, the emphasis on the distributed architecture and the networked communication, and we discuss the back-end database that is used to store information about car owners, cars, and charging stations.
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
Electric autonomous vehicles. https://eav.innopolis.university/. Accessed 23 Aug 2019
Sit back, relax, and enjoy a ride through the history of self-driving cars. https://www.digitaltrends.com/cars/history-of-self-driving-cars-milestones/. Accessed 23 Aug 2019
Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Technical report, U.S. Department of Transportation, 1200 New Jersey Ave, SE, Washington, DC 20590, USA, February 2015. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115
Adrion, W.R., Branstad, M.A., Cherniavsky, J.C.: Validation, verification, and testing of computer software. ACM Comput. Surv. (CSUR) 14(2), 159–192 (1982)
Afanasyev, I., Kolotov, A., Rezin, R., Danilov, K., Mazzara, M., Chakraborty, S., Kashevnik, A., Chechulin, A., Kapitonov, A., Jotsov, V., et al.: Towards blockchain-based multi-agent robotic systems: analysis, classification and applications. arXiv preprint arXiv:1907.07433 (2019)
Benčić, F.M., Žarko, I.P.: Distributed ledger technology: blockchain compared to directed acyclic graph. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 1569–1570. IEEE (2018)
Burkhardt, D., Werling, M., Lasi, H.: Distributed ledger. In: 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–9, June 2018
Dragoni, N., Giaretta, A., Mazzara, M.: The internet of hackable things. In: Proceedings of 5th International Conference in Software Engineering for Defence Applications - SEDA 2016, Rome, Italy, 10 May 2016, pp. 129–140 (2016)
Dragoni, N., Lanese, I., Larsen, S.T., Mazzara, M., Mustafin, R., Safina, L.: Microservices: how to make your application scale. In: Perspectives of System Informatics - 11th International Andrei P. Ershov Informatics Conference, PSI 2017, Moscow, Russia, 27–29 June 2017, pp. 95–104 (2017). Revised Selected Papers
Elbanna, Z.: Innopolis electric autonomous vehicle - Inno EAV (2019)
Fisher, M.S.: Software Verification and Validation: An Engineering and Scientific Approach. Springer, Heidelberg (2007)
Hua, S., Zhou, E., Pi, B., Sun, J., Nomura, Y., Kurihara, H.: Apply blockchain technology to electric vehicle battery refueling. In: Proceedings of the 51st International Conference on System Sciences (2018)
Hussain, R., Lee, J., Zeadally, S.: Autonomous cars: social and economic implications. IT Prof. 20(6), 70–77 (2018)
Hussain, R., Zeadally, S.: Autonomous cars: research results, issues, and future challenges. IEEE Commun. Surv. Tutorials 21(2), 1275–1313 (2019)
Hussain, R., Son, J., Kim, D., Nogueira, M., Oh, H., Tokuta, A.O., Seo, J.: PBF: a new privacy-aware billing framework for online electric vehicles with bidirectional auditability (2017)
ISO/IEC: Software product quality (2019). https://iso25000.com/index.php/en/iso-25000-standards/iso-25010
Kyriakidis, M., Happee, R., de Winter, J.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transp. Res. Part F: Psychol. Behav. 32, 127–140 (2015)
Lwakatare, L.E., Kilamo, T., Karvonen, T., Sauvola, T., Heikkilä, V., Itkonen, J., Kuvaja, P., Mikkonen, T., Oivo, M., Lassenius, C.: Devops in practice: a multiple case study of five companies. Inf. Softw. Technol. 114, 217–230 (2019)
Miranda, E.: Time boxing planning: buffered Moscow rules. ACM SIGSOFT Softw. Eng. Notes 36, 1–5 (2011). https://doi.org/10.1145/2047414.2047428
Rouse, M.: What is machine to machine (2018). https://internetofthingsagenda.techtarget.com
Strugar, D., Hussain, R., Mazzara, M., Rivera, V., Afanasyev, I., Lee, J.: An architecture for distributed ledger-based M2M auditing for electric autonomous vehicles. In: Web, Artificial Intelligence and Network Applications - Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications, AINA Workshops 2019, Matsue, Japan, 27–29 March 2019, pp. 116–128 (2019)
Strugar, D., Hussain, R., Mazzara, M., Rivera, V., Lee, J.Y., Mustafin, R.: On M2M micropayments: a case study of electric autonomous vehicles. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1697–1700. IEEE (2018)
Ting, Z., Jianjun, H., Li, S., Jianfeng, L., Yan, M.: Study on mobility models in vehicular communication system. In: 2009 2nd IEEE International Conference on Broadband Network Multimedia Technology, pp. 57–61, October 2009. https://doi.org/10.1109/ICBNMT.2009.5347824
Zhu, L., Yu, F.R., Wang, Y., Ning, B., Tang, T.: Big data analytics in intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 20(1), 383–398 (2019). https://doi.org/10.1109/TITS.2018.2815678
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
Elbanna, Z. et al. (2020). A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-44038-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44037-4
Online ISBN: 978-3-030-44038-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)