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

Hemavathi, Sreenatha Reddy Akhila, Youseef Alotaibi, Osamah Ibrahim Khalaf and Saleh Alghamdi
Additional contact information
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

Energies, 2022, vol. 15, issue 6, 1-27

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.

Keywords: Deep-Q learning; RSU; URLLC; DSRC; E2E Delay; IoV; Markov Decision Process; authentication (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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