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

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.

1. Introduction

Though 5G is yet to be deployed widely, it appears that 5G stands to affect nearly every part of our day to day life, be it health care, education, transportation, industry, smart grids, entertainment and media, etc. 5G is expected to power the future of people’s mobility through Internet of Vehicles (IoV) technology. It can be called an Internet on wheels and can allow vehicles to communicate with their drivers, with other vehicles, with traffic signals and roadside infrastructure, or with any other internet-connected item. Features like the ability to stream full video games in vehicles, crash prevention, traffic flow monitoring, safe navigation, intelligent vehicle control, vehicle autonomy, and even electronic toll collection makes IoV one of the attractive applications of 5G.

The concept of connected vehicles is known as vehicular communications or V2X. V2X includes vehicle-to-infrastructure/network (V2I/N) and vehicle-to-vehicle (V2V) [1,2]. IoV is supported by Dedicated Short-Range Communication (DSRC) and cellular mobile communication systems for effective implementation. DSRC standards in the United States and ITS-G5 standards produced by the European Telecommunications Standards Institute (ETSI) have both been created during the last decade to coordinate with the activities of many stakeholders in vehicular communications. The ITS and smart cities protocols are grounded in IEEE 802.11p technology, which offers the framework for vehicle ad hoc network communications. The 3rd Generation Partnership Project (3GPP) has recently been working on integrating V2X services into LTE and future 5G cellular networks [3,4,5].

The combination of DSRC and cellular communication is capable of improving intelligence and independent driving capability in IoV, by providing safe, intelligent, comfortable, and efficient comprehensive services. With the rapid pace of 5G development, it has been possible to meet different performance criteria’s in varied application scenarios [6]. The intelligent transportation system (ITS) is expected to see a boom in the upcoming days due to the unimaginable speeds that can be attained by 5G. Some of the communication requirements of ITS are the low communication delay and high reliability of vehicle status data [7]. To provide a seamless mobility experience to the user, the handoff strategy, similar to cellular communication, is used in IoV. The handoffs may be frequent due to the small coverage area of RSUs and the dynamic nature of the vehicles. V2X has become a key enabler for bringing an innovative level of connectivity to automobiles, especially when combined with onboard computing and sensor technologies.

Since Internet of Vehicles is characterized by a high level of mobility and dynamic changes in topology, handoff is one of the key technologies enabling efficient deployment of these connected and autonomous vehicles for providing seamless communication. The term handoff refers to transferring the active communication from one Road Side Unit to another seamlessly. This could be horizontal or vertical. It is termed horizontal handoff if network access technologies across the RSUs are the same and vertical handoff if access technologies across the RSUs are different.

With self-driving vehicles changing the transportation scenario, connected vehicles are becoming the core of transportation systems. This calls for redefining business models, be it transportation sector, energy sector, and even government regulations. Under such a connected scenario, security in IoV becomes one of the most important entities, since any system failure can directly impact a user’s safety. Hence, secure and consistent authentication and low computation overhead are required for the amalgamation of 5G networks and vehicular network technology.

In IoV, communication is between the RSU and vehicles [8,9]. The information is transmitted over an unsecure wireless channel between two communication parties that are highly vulnerable to attacks. Having an efficient authentication scheme ensures that only authorized users are allowed into the network, and it is effective against active and passive attacks, hence satisfying the need for a secure design. In addition, it ensures that communication is amongst trustworthy entities only. For secure communication, mutual authentication among the involved entities is performed by broadcasting periodic safety messages. These messages include critical information about vehicle speed and location, traffic conditions, and braking status. Hence, it becomes significant to guarantee that an acquired safety message comes from legitimate vehicles and is not altered via attackers, as any modification and replaying of the broadcasted messages can be disastrous to drivers [10].

Every vehicle in IoV is viewed as an intelligent object with control units, computing facilities, sensing platforms, and storage that are accessible via V2X. In IoV, communication is over a wireless link that is supported by the 5G technology. Nonetheless, due to densification and with limited resources, it is difficult to schedule the resource to collect and process real-time requests from the vehicles, making it difficult to guarantee efficient and reliable data transmission by traditional IoV communications. Through resource sharing, it will be possible to increase the execution speed of a computing task and overcome the insufficient computing resource problem for the vehicle, thus providing ultra-low latency, high bandwidth, higher responsiveness, and throughput to the users [11,12]. In reality, the network status and available resources of RSU vary dynamically due to the mobility of vehicles. This results in frequent handoffs between the vehicle and the RSU. To coordinate with V2I links, an effective resource allocation method is required. In traditional methods, resource allocation may be stated as optimization problem using global network information, where QoS requirements of V2I act as a constraint [13,14].

IoV has turned into a new, hotly contested arena for innovations in automobile industries. It calls for the development of applications such as road safety, infotainment, and efficient traffic management. With the foremost use of a vehicle still being driving, it is the automakers who will be responsible for putting IoV technologies in the vehicle [15,16,17]. Artificial Intelligence (AI) can explore and handle the unpredictable requirements of IoV to achieve this goal. AI with Machine Learning (ML) and Deep Learning (DL) technology can assist 5G networks in anticipating and managing variable network traffic. Reinforcement Learning (RL), being a type of ML, can effectively solve decision-making problems [18].

In this work, RL based on the Markov Decision Policy (RL-MDP) has been used for making decisions in selecting the best RSU in a reasonable time. However, this is not preferable for huge data sets since it might not lead to making the right decision. Once RSU is selected, the authentication of vehicles is done using the Deep Sparse Stacked Autoencoder Network (DS2AN). This technique has resulted in less computation complexity and communication cost, and resource allocation for the vehicle is done, using Deep Reinforcement Learning (DRL), during handoff. The contributions of this study are as follows:

• To implement a method for reducing handoff delay and to improve QoS parameter performance.

• To develop a secure and fast authentication method using DS2AN during the change of RSU.

• To implement the DQL method to analyze the node activity and resource for IoV.

• To develop the Bellman-Ford algorithm to search the shortest path between communication resources.

The paper is structured as follows: Related works are discussed in Section 2. Section 3 describes the methodology adopted in this study, with mathematical modeling of the RL-MDP for the selection of RSU, DS2AN for authentication, and DRL based resource allocation during handoff. A discussion on the results obtained is presented in Section 4, followed by the conclusion in Section 5.

2. Related Work

Awan et al. [19] have proposed a RSU selection method using a dynamic edge-backup node concept in IoV communication. The proposed method is based on clustering. During the discovery of a cluster head, the vehicle sends a message to its neighbors; if no response is received, it initiates a group formation process. A cluster is formed among the vehicles that are moving in the same direction; they are grouped into one cluster. Messages, which contain the location, ID, and speed, are transmitted to the peers in the group. During cluster formation, two nodes, the head node and edge backup node, are selected depending on a score. The calculation of score is dependent on parameters like storage capacity, communication range, and energy. When a new node enters the cluster, a new score will be calculated to decide on the new cluster head. The edge node provides support to the cluster head upon failure of a head node. The cluster head decides on the RSU, with which a connection is established thus enabling communication of the peers. This clustering technique has resulted in higher reliability due to the use of an edge node and an improved throughput. However, there has been no significant improvement achieved in the packet loss rate due to the overhead incurred by the cluster head. Still, seamless handoff of the cluster head is achieved due to the edge node concept used by the author. The authors in [20,21] have discussed Energy-Efficient system modeling for Ad-Hoc Networks and in [22] multipath routing protocols for MANET.

Hussain et al., in [23], have proposed a new method for network selection called the Fuzzy Convolution Neural Network. The handoff decision, based on performance metrics like vehicle speed and signal strength, is made by utilizing the Shannon entropy-based Q-learning algorithm. Metrics such as data type, spacing, vehicular density, number of obstructions, and signal strength have been used for best network selection. V2V chain routing has been achieved through the Jellyfish optimization algorithm in order to find an optimal route amongst the available routes. The authors have been able to achieve an improved throughput by over 15–20%, a minimized delay, and packet loss. The Fuzzy Convolution Neural Network has helped speed up the network selection process.

The work proposed by Fang Jia et al. [24] emphasizes the BUS-aided selection of RSUs, built upon software-defined networking (SDN) and evolutionary games. The authors have concentrated on selecting the best RSU in overlapping areas of RSU. An SDN controller is able to communicate with the vehicles, and fixed and mobile RSUs (BUSES). It gathers data related to the load, throughput, location ID, and bandwidth availability of RSUs, and the ID, route, location, speed, throughput, load, and bandwidth of the BUSES. Then, using the evolutionary game theory concept, an RSU which provides best connectivity is selected. The authors have been able to achieve load balance along with an improved throughput.

Due to the dynamic nature of the network, vehicles exchange information either with an RSU or other moving vehicles frequently. After selecting a suitable RSU for handoff, the vehicle authenticates itself to the RSU and in turn checks the RSU’s authenticity. Once mutually authenticated, resource allocation by RSU to the vehicle is done successfully. In [25], the authors proposed a secure and efficient authentication protocol using cryptographic analysis. The authors have successfully addressed various attacks like the impersonation, man in the middle, smart card theft, session key disclosure, and replay attacks. The algorithm has resulted in enhanced security and has been able to preserve a low communication cost of 138 bytes and a computation cost of 2.262 ms, as compared to related schemes.

In [26], a mutual authentication method based on the identity of the RSU and vehicle has been proposed. The system related information is stored in the RSU using the bilinear pair mapping theory and elliptic curve encryption algorithm. The use of the bilinear pair mapping theory and elliptic curve encryption algorithm has guaranteed the irreversibility of group operation, making it impossible for attackers to have access to the network through reverse engineering. The legitimacy of the communicating nodes, RSU, and OBU (On Board Unit) is ensured through mutual authentication using IDs, shared keys, and the handshake principle.

Ping Li et al. [27] formulated a problem based on resource allocation to optimize the throughput of vehicular user equipment (VUEs), while balancing vehicular communications reliability and latency and Quality of Service (QoS) for Wi-Fi networks using networks that coexist with VUE and Wi-Fi User Equipment (WUEs). Authors have employed the listen-before-talk (LBT) method, which requires VUEs to regularly check for other occupants in the channel before transmitting. They estimated the ideal number of offload vehicle users and used the Lagrange Dual Method to convert the optimization issue into a convex optimization problem. Experimentation has proved that their approach performs better when compared to the Greedy method, in terms of throughput.

Pressas et al. [28] investigated the broadcast transmission in V2V using the IEEE 802.11p standard for DRSCs for a contention-based MAC protocol. With a higher packet delivery ratio and lower latency than 4G, the IEEE 802.11P protocol can provide superior performance. The authors of this work have provided a study to handle scalability issues keeping in mind the need for an ML-based approach. The authors have been able to demonstrate an effective data packet exchange, discover the best contention window for broadcast in V2V communication, along with an increased packet delivery ratio and throughput. In comparison with central TBSs, the RSUs are small and have limited resources. In reality, the network status and RSUs’ resources change regularly during mobility of the vehicles. As a result, a time-varying resource allocation method that takes into account the task demands and dynamic status of vehicles is required. A novel method for network selection using the RL-MDP, a fast and secure authentication method using DS2AN and resource allocation using DQL has been proposed.