IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12800-d935784.html
   My bibliography  Save this article

Blockchain and IoT-Driven Optimized Consensus Mechanism for Electric Vehicle Scheduling at Charging Stations

Author

Listed:
  • Riya Kakkar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India)

  • Rajesh Gupta

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India)

  • Smita Agrawal

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India)

  • Sudeep Tanwar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India)

  • Ahmed Altameem

    (Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia)

  • Torki Altameem

    (Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia)

  • Ravi Sharma

    (Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248001, India)

  • Florin-Emilian Turcanu

    (Department of Building Services, Faculty of Civil Engineering and Building Services, Gheorghe Asachi Technical University of Iasi, 700050 Jassy, Romania)

  • Maria Simona Raboaca

    (National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Vâlcea, Uzinei Street, No. 4, P.O. Box 7 Râureni, 240050 Râmnicu Vâlcea, Romania
    Faculty of Civil Engineering, Civil Engineering and Management Department, Technical University of Cluj—Napoca, C-tin Daicoviciu Street, No. 15, 400020 Cluj-Napoca, Romania
    University Politehnica of Bucharest, Splaiul Independentei Street no. 313, 060042 Bucharest, Romania)

Abstract

The emerging demand for electric vehicles in urban cities leads to the need to install a huge number of charging stations. With this requirement, electric vehicle coordination and scheduling at charging stations in real-time becomes highly tedious. Thus, there is a need for an efficient scheduling mechanism for electric vehicle charging at charging stations. This paper proposes a novel blockchain and Internet of Things-based consensus mechanism called COME for secure and trustable electric vehicle scheduling at charging stations. The proposed mechanism is intending to resolve conflicts at charging stations. The integrated InterPlanetary File System protocol facilitates a cost-efficient mechanism with minimized bandwidth for electric vehicle scheduling. The proposed mechanism ensures that there is no loss for either the electric vehicle or the charging station. We formulate different scenarios for electric vehicle charging and apply different scheduling algorithms, including first-come first-served, longest remaining time first, and coalition game theory. The performance of the proposed COME consensus mechanism is estimated by comparing it with the practical Byzantine Fault Tolerance consensus protocol and traditional systems based on the charging demand, wait time, conflict resolution, scalability, and InterPlanetary File System bandwidth parameters. The performance results show that the proposed COME consensus mechanism ensures that electric vehicles can have their vehicle charged without any conflict and that the charging station can be satisfied in terms of profit. Moreover, the proposed COME consensus mechanism outperforms the both practical Byzantine Fault Tolerance consensus protocol and the traditional system in terms of scalability and conflict resolution along with additional parameters such as wait time, charging demand, and bandwidth analysis.

Suggested Citation

  • Riya Kakkar & Rajesh Gupta & Smita Agrawal & Sudeep Tanwar & Ahmed Altameem & Torki Altameem & Ravi Sharma & Florin-Emilian Turcanu & Maria Simona Raboaca, 2022. "Blockchain and IoT-Driven Optimized Consensus Mechanism for Electric Vehicle Scheduling at Charging Stations," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12800-:d:935784
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12800/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12800/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.
    2. Maxim A. Dulebenets, 2018. "A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility," Sustainability, MDPI, vol. 10(5), pages 1-23, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    2. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    3. Dulebenets, Maxim A., 2019. "A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility," International Journal of Production Economics, Elsevier, vol. 212(C), pages 236-258.
    4. Oleksandra Osypchuk & Katarzyna Sosik, 2021. "Impact of the Construction Supplies Implementation on Road Safety in the City Center: A Case Study of the City of Szczecin," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    5. Sundaram Manikandan & Ganesan Kaliyaperumal & Saqib Hakak & Thippa Reddy Gadekallu, 2022. "Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    6. Rocío González-Sánchez & Davide Settembre-Blundo & Anna Maria Ferrari & Fernando E. García-Muiña, 2020. "Main Dimensions in the Building of the Circular Supply Chain: A Literature Review," Sustainability, MDPI, vol. 12(6), pages 1-25, March.
    7. Haili Zhang & Michael Song & Xiaoming Yang & Ping Li, 2019. "What are Important Technologies for Sustainable Development in the Trucking Industries of Emerging Markets? Differences between Organizational and Individual Buyers," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    8. Reza Kiani Mavi & Mark Goh & Neda Kiani Mavi & Ferry Jie & Kerry Brown & Sharon Biermann & Ahmad A. Khanfar, 2020. "Cross-Docking: A Systematic Literature Review," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    9. Davide Falabretti & Francesco Gulotta, 2022. "A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    10. Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    11. Tarik Chargui & Abdelghani Bekrar & Mohamed Reghioui & Damien Trentesaux, 2019. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption," Sustainability, MDPI, vol. 11(11), pages 1-23, June.
    12. Masoud Kavoosi & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Ren Moses & John Sobanjo & Eren E. Ozguven, 2020. "Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida," Energies, MDPI, vol. 13(6), pages 1-28, March.
    13. Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    14. Praveen Prakash Singh & Fushuan Wen & Ivo Palu & Sulabh Sachan & Sanchari Deb, 2022. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions," Energies, MDPI, vol. 16(1), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12800-:d:935784. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.