A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles

Ziyad Elbanna; Ilya Afanasyev; Luiz Jonatã Pires de Araújo; Rasheed Hussain; Mansur Khazeev; Joseph Lamptey; Manuel Mazzara; Swati Megha; Diksha Moolchandani; Dragos Strugar

doi:10.1007/978-3-030-44038-1_4

A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles

Ziyad Elbanna, Ilya Afanasyev, Luiz Jonatã Pires de Araújo^*, Rasheed Hussain, Mansur Khazeev, Joseph Lamptey, Manuel Mazzara, Swati Megha, Diksha Moolchandani, Dragos Strugar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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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.

Original language	English
Title of host publication	Web, Artificial Intelligence and Network Applications
Subtitle of host publication	Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020)
Editors	Leonard Barolli, Flora Amato, Francesco Moscato, Tomoya Enokido, Makoto Takizawa
Publisher	Springer
Pages	34-45
Number of pages	12
ISBN (Electronic)	978-3-030-44038-1
ISBN (Print)	978-3-030-44037-4
DOIs	https://doi.org/10.1007/978-3-030-44038-1_4
Publication status	Published - 31 Mar 2020
Event	Workshops of the 34th International Conference on Advanced Information Networking and Applications, WAINA 2020 - Caserta, Italy Duration: 15 Apr 2020 → 17 Apr 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1150 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Workshops of the 34th International Conference on Advanced Information Networking and Applications, WAINA 2020
Country/Territory	Italy
City	Caserta
Period	15/04/20 → 17/04/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Charging station
Electric Autonomous Vehicles
Machine-to-machine economy
Networking
Software process and design

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-44038-1_4

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Elbanna, Z., Afanasyev, I., de Araújo, L. J. P., Hussain, R., Khazeev, M., Lamptey, J., Mazzara, M., Megha, S., Moolchandani, D., & Strugar, D. (2020). A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles. In L. Barolli, F. Amato, F. Moscato, T. Enokido, & M. Takizawa (Eds.), Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020) (pp. 34-45). (Advances in Intelligent Systems and Computing; Vol. 1150 AISC). Springer. https://doi.org/10.1007/978-3-030-44038-1_4

Elbanna, Ziyad ; Afanasyev, Ilya ; de Araújo, Luiz Jonatã Pires et al. / A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles. Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020). editor / Leonard Barolli ; Flora Amato ; Francesco Moscato ; Tomoya Enokido ; Makoto Takizawa. Springer, 2020. pp. 34-45 (Advances in Intelligent Systems and Computing).

@inproceedings{e54e4a92a7dc467eba2845263dc6a832,

title = "A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles",

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.",

keywords = "Charging station, Electric Autonomous Vehicles, Machine-to-machine economy, Networking, Software process and design",

author = "Ziyad Elbanna and Ilya Afanasyev and \{de Ara{\'u}jo\}, \{Luiz Jonat{\~a} Pires\} and Rasheed Hussain and Mansur Khazeev and Joseph Lamptey and Manuel Mazzara and Swati Megha and Diksha Moolchandani and Dragos Strugar",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Workshops of the 34th International Conference on Advanced Information Networking and Applications, WAINA 2020 ; Conference date: 15-04-2020 Through 17-04-2020",

year = "2020",

month = mar,

day = "31",

doi = "10.1007/978-3-030-44038-1\_4",

language = "English",

isbn = "978-3-030-44037-4",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "34--45",

editor = "Leonard Barolli and Flora Amato and Francesco Moscato and Tomoya Enokido and Makoto Takizawa",

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Elbanna, Z, Afanasyev, I, de Araújo, LJP, Hussain, R, Khazeev, M, Lamptey, J, Mazzara, M, Megha, S, Moolchandani, D & Strugar, D 2020, A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles. in L Barolli, F Amato, F Moscato, T Enokido & M Takizawa (eds), Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020). Advances in Intelligent Systems and Computing, vol. 1150 AISC, Springer, pp. 34-45, Workshops of the 34th International Conference on Advanced Information Networking and Applications, WAINA 2020, Caserta, Italy, 15/04/20. https://doi.org/10.1007/978-3-030-44038-1_4

A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles. / Elbanna, Ziyad; Afanasyev, Ilya; de Araújo, Luiz Jonatã Pires et al.
Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020). ed. / Leonard Barolli; Flora Amato; Francesco Moscato; Tomoya Enokido; Makoto Takizawa. Springer, 2020. p. 34-45 (Advances in Intelligent Systems and Computing; Vol. 1150 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles

AU - Elbanna, Ziyad

AU - Afanasyev, Ilya

AU - de Araújo, Luiz Jonatã Pires

AU - Hussain, Rasheed

AU - Khazeev, Mansur

AU - Lamptey, Joseph

AU - Mazzara, Manuel

AU - Megha, Swati

AU - Moolchandani, Diksha

AU - Strugar, Dragos

PY - 2020/3/31

Y1 - 2020/3/31

N2 - 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.

AB - 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.

KW - Charging station

KW - Electric Autonomous Vehicles

KW - Machine-to-machine economy

KW - Networking

KW - Software process and design

UR - https://www.scopus.com/pages/publications/85083958029

U2 - 10.1007/978-3-030-44038-1_4

DO - 10.1007/978-3-030-44038-1_4

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85083958029

SN - 978-3-030-44037-4

T3 - Advances in Intelligent Systems and Computing

SP - 34

EP - 45

BT - Web, Artificial Intelligence and Network Applications

A2 - Barolli, Leonard

A2 - Amato, Flora

A2 - Moscato, Francesco

A2 - Enokido, Tomoya

A2 - Takizawa, Makoto

PB - Springer

T2 - Workshops of the 34th International Conference on Advanced Information Networking and Applications, WAINA 2020

Y2 - 15 April 2020 through 17 April 2020

ER -

Elbanna Z, Afanasyev I, de Araújo LJP, Hussain R, Khazeev M, Lamptey J et al. A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles. In Barolli L, Amato F, Moscato F, Enokido T, Takizawa M, editors, Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020). Springer. 2020. p. 34-45. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-44038-1_4

A Machine to Machine Framework for the Charging of Electric Autonomous Vehicles

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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