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Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Author

Listed:
  • Hemavathi

    (Department of Electronics and Communication Engineering, B.M.S. College of Engineering, Bengaluru 560019, India)

  • Sreenatha Reddy Akhila

    (Department of Electronics and Communication Engineering, B.M.S. College of Engineering, Bengaluru 560019, India)

  • Youseef Alotaibi

    (Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Osamah Ibrahim Khalaf

    (Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad 10001, Iraq)

  • Saleh Alghamdi

    (Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

Abstract

One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

Suggested Citation

  • Hemavathi & Sreenatha Reddy Akhila & Youseef Alotaibi & Osamah Ibrahim Khalaf & Saleh Alghamdi, 2022. "Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning," Energies, MDPI, vol. 15(6), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2006-:d:767571
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    References listed on IDEAS

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    1. Satheeshkumar Palanisamy & Balakumaran Thangaraju & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "A Novel Approach of Design and Analysis of a Hexagonal Fractal Antenna Array (HFAA) for Next-Generation Wireless Communication," Energies, MDPI, vol. 14(19), pages 1-18, September.
    2. Haruna Chiroma & Shafi’i M. Abdulhamid & Ibrahim A. T. Hashem & Kayode S. Adewole & Absalom E. Ezugwu & Saidu Abubakar & Liyana Shuib, 2021. "Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-20, November.
    3. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
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    Cited by:

    1. Mariusz Kostrzewski & Magdalena Marczewska & Lorna Uden, 2023. "The Internet of Vehicles and Sustainability—Reflections on Environmental, Social, and Corporate Governance," Energies, MDPI, vol. 16(7), pages 1-20, April.
    2. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    3. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.

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