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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
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    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.
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