In today's scenario, every individual possesses substantial complications relating to transportation systems such as congestion, vehicle parking difficulties, pollution caused by an increased level of carbon dioxide emission, longer traveling times, road accidents, and so on. These consequences are due to the rising number of vehicular systems and rapid urbanization processes. This leads to the development of modern technologies such as Intelligent Transport Systems (ITS), Vehicular ad-hoc Networks (VANET), VANET cloud (VC), and Internet of Vehicles (IoV). Among all these techniques, IoV remains the most prominent and emerging one. IoV is a series of interconnected vehicles that communicate with each other through a common public network. It is a highly integrated application of two major domains IoT and ITS. Communication across the IoV network includes three different types such as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to Pedestrian (V2P). Smart vehicles with progressive communication abilities do not only communicate with broadcast and navigation satellites but also with various smart devices such as roadside units, smart vehicles, and passenger smartphones. Since IoV has widespread interconnected networks with numerous users it is obvious that there is an increased risk of security and privacy measures. These security and privacy concerns may lead to serious consequences if they are not addressed and dealt with in an appropriate manner. Some of the most common security and privacy issues across the IoV environment include tracking vehicle locations, hardware tampering, unauthorized data access, message modification, and fabrication. The intruders can even introduce an ambiguity across the network and steal the confidential data with the inevitable loss of data integrity and privacy features. Thus, advanced security measures for IoV systems have become the most essential requirement.

In recent years, the concept of cybersecurity is immensely applied across various domains to protect networks, data, programs, and devices from security vulnerabilities and unauthorized data access. IoV transmits sensitive data across networks and to other devices for various means of confidential purposes. The possibility of cyber-attacks is comparatively higher when data transmission takes place more frequently through various nodes of IoV systems. Thus, the application of cybersecurity measures for IoV provides the most prominent solution for security and privacy measures. Most of the existing cybersecurity mechanisms deal with critical system components and provide a solution to the well-known security threats.

This special issue aims at presenting the current state-of-the-art research and future trends on various aspects of cybersecurity mechanisms for IOV systems with improved security and privacy features. Further, it brings out the various researchers and industry people from different fields of computer science to come up with novel and effective cyber-security solutions for security protection in IoV systems. The main areas covered by this special issue or main topics cover methodologies, modeling, analysis, and newly introduced applications. Besides the latest research achievements, this special issue also deals with innovative commercial management systems, innovative commercial applications of IoV technology, and experience in applying recent research advances to real-world problems.

The response of the research community has been significant: 79 original contributions have been submitted for consideration. Among those, 11 papers were accepted after going through a rigorous review process. All of the accepted research papers have significant elements of novelty and add reasonable contributions to the existing research works in this domain. They not only provide novel ideas and state-of-the-art techniques in the field, but also stimulate future research in sustainable intelligent transportation systems.

The security of communications among vehicles, infrastructures, and roads is vital. The existing IoV systems based on symmetric cryptographic schemes and public key signature schemes are vulnerable to quantum attacks since they can be broken by powerful quantum computers. The paper by Haibo Yi, Ruinan Chi, Xin Huang, Xuejun Cai, and Zhe Nie, entitled “Improving Security of Internet of Vehicles Based on Post-Quantum Signatures with Systolic Divisions” [1], proposed a systolic architecture for improving the IoT security based post-quantum signatures with systolic divisions. Experiments were conducted on a Field-Programmable Gate Array (FPGA). The results confirm that the proposed method can be further applied to various applications like solving system of linear equations and cryptographic applications for IoT security.

An important security requirement in automotive networks is to authenticate, sign, and verify thousands of short messages per second by each vehicle. The paper by Mohamad Ali Mehrabi and Alireza Jolfaei, entitled “Efficient Cryptographic Hardware for Safety Message Verification in Internet of Connected Vehicles” [2], proposed high-speed Residue Number System Montgomery modular reduction units with parallel computing to reduce the latency of the field modular operations. Compared to the literature, the proposed scheme provides faster computation without compromising the security level. Experimental results confirm that the proposed architecture can effectively reduce the latency of the point multiplication and can address the latency requirement of safety message verification under an average urban traffic density.

Internet of Vehicles (IoV) communication platform provides seamless information exchange facilities in a dynamic mobile city environment. Cyber-Security is a primary concern in accessing autonomous information from the distributed resources due to anonymity and different types of targeted adversaries. The paper by Gunasekaran Manogaran, Bharat S. Rawal, Vijayalakshmi Saravanan, Priyan Malarvizhi Kumar, Qin Xin, and P. Mohamed Shakeel, entitled “Token-based Authorization and Authentication for Secure Internet of Vehicles Communication” [3], proposed token-based authorization and authentication for securing IoV communications. The proposed method relies on blockchain technology and random forest learning for authorization and key management for authentication, respectively. Besides, the sustained block chain based token assignment helps to retain the communication ratio by reducing the V2V losses. The performance of the proposed method is assessed using simulations by varying the vehicles density, error rate, and so on.

In recent decades, there has been rigorous research work on the area of the Intelligent Transportation System (ITS) which includes developing secure systems for vehicles. Recognizing theft and preventing the thief from driving away the vehicle is an important task and identifying the owner/driver of the vehicle is a crucial problem to solve. The paper by Chandrasekar Ravi, Anmol Tigga, G. Thippa Reddy, Saqib Hakak, and Mamoun Alazab, entitled “Driver Identification Using Optimized Deep Learning Model in Smart Transportation” [4], proposed a deep-learning based driver identification system for the mitigation of theft. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The proposed concept can be extended further to provide enhanced security in intelligent transportation systems within smart cities.

Pedestrian trajectory prediction is a challenging task due to the intrinsic complexity of human behaviors. Predicting the future path of a pedestrian in a crowd is difficult because it requires a processing of multiple parameters, including the social neighbors and physical environments. The paper by Song Xiao, Kai Chen, Xiaoxiang Ren, and Haitao Yuan, entitled “Pedestrian Trajectory Prediction using Facial Keypoints based Convolutional Encoder-Decoder Network” [5], proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her facial keypoints. An end-to-end facial keypoints-based convolutional encoder-decoder network is designed. It uses a multi-channel tensor to represent the relative positions and categories of heterogeneous traffic objects around the subject pedestrian. Experimental results on public benchmarks demonstrate that the proposed technique outperforms state-of-the-art methods.

In recent years, cloud computing has become a promising solution for data storage and processing in IoV scenarios. However, the cloud-based IoV still faces many security challenges as a new cutting-edge technique. How to ensure the integrity of IoV data outsourced to the cloud is one of the biggest concerns. The paper by Hui Tian, Fang Peng, Hanyu Quan, and Chin-Chen Chang, entitled “Identity-based Public Auditing for Cloud Storage of Internet-of-Vehicles Data” [6], proposed an identity-based public auditing scheme for cloud storage of IoV data, which can fully achieve the essential function and security requirements, such as classified auditing, multi-source auditing, and privacy protection. The authors designed a new authenticated data structure, called data mapping table, to track the distribution of each type of IoV data to ensure fine and rapid audits. The theoretical analyses and experimental results demonstrate that our scheme can securely and efficiently realize public auditing for IoV data and outperforms the previous ones in both the computation and communication overheads in most cases.

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithms, network planning, anomaly, and intrusion detection. The paper by Xiaojie Wang, Laisen Lie, Zhaolong Ning, Lei Guo, Guoyin Wang, Xinbo Gao, and Neeraj Kumar, entitled “Deep Learning-based Network Traffic Prediction for Secure Backbone Networks in Internet of Vehicles” [7], proposed a deep learning-based method for end-to-end network traffic prediction in IoV backbone networks. The constructed system considers the spatio-temporal feature of network traffic and can capture the long-range dependence of network traffic. A threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic data set.

A growing number of smart vehicles makes it possible to envision a crowdsensing service where vehicles can share video data of their surroundings for seeking out traffic conditions and car accidents ahead. However, the service may need to deal with situations in which malicious vehicles propagate false information to divert other vehicles away to arrive at the destinations earlier or lead them to dangerous locations. The paper by Si Young Jang, Sung Kyu Park, Jin Hee Cho, and Dongman Lee, entitled “CARES: Context-Aware Trust Estimation for Realtime Crowdsensing Services in Vehicular Edge Networks” [8], proposed a context-aware trust estimation scheme that can allow roadside units in a vehicular edge network to provide real-time crowdsensing services in a reliable manner by selectively using information from trustworthy sources. Simulation results show that the proposed technique outperforms the state-of-the-art rule-based trust adjustment schemes without prior knowledge of the distribution of trust values of incoming vehicles and is also more resilient to outside and inside attacks.

Nowadays, a growing number of computation intensive applications appear in our daily life. Those applications make the loads of both the core network and the mobile devices, in terms of energy and bandwidth, hugely increase. Offloading computation intensive tasks to edge cloud is proposed to address this issue. However, several critical issues in parked vehicle assisted mobile edge computing would result in low reliable edge service. The open environment would bring about uncertainty, and data privacy is hard to ensure. The paper by Ao Zhou, Xiao Ma, Siyi Gao, and Shangguang Wang, entitled “Providing Reliable Service for Parked Vehicle Assisted Mobile Edge Computing” [9], proposed a resource management scheme to address the privacy issue. This study reviews the execution model of computation and communication in parked vehicle assisted computation offloading. The authors formulate the problem into a mixed-integer nonlinear programming. They decompose the original problem into two sub-problems with lower complexity, and related algorithms are given to deal with the sub-problems. Simulation results demonstrate the effectiveness of the proposed solution.

Mobile network is a collection of devices with dynamic behavior where devices keep moving, which may lead to the network track being connected or disconnected. This type of network is called Intermittently Connected Mobile Network (ICMN). The paper by Ramesh Sekaran, Fadi Al-Turjman, Rizwan Patan, and Velmani Ramasamy, entitled “Tripartite Transmitting Methodology for Intermittently Connected Mobile Network (ICMN)” [10], proposed a protocol for Intermittently Connected Mobile Network which provides an expected level of security. The proposed technique gives an optimal result following various network characteristics. Algorithms embedded with productive routing provide maximum security. Comparative analysis of results is also given by three algorithms. Results are pointed out by analysis taken from spreading false devices into the network and their effectiveness at the worst case.

Route planning helps a vehicle to share a message with the road side units (RSUs) on its path in advance, which greatly speeds the authentication between the vehicle and the RSUs when the vehicle enters the RSUs’ coverage. The paper by Yangfan Liang, Yining Liu, and Brij B. Gupta, entitled “PPRP: Preserving-Privacy Route Planning Scheme in VANETs” [11], proposed a privacy-preserving route planning scheme for VANETs, which protects vehicles’ route privacy from certification authority (CA). The proposed scheme ensures that CA does not know which RSUs’ information is shared with the vehicle. The vehicle can effectively communicate with adjacent vehicles with the assistance of RSUs. Compared with recent schemes, the proposed scheme not only met basic security requirements such as confidentiality, authentication, integrity, and traceability in VANET, but also implemented privacy-preserving in route planning.

All of the above papers address either technical issues in IoV technologies or Cyber Security or propose novel application models in the various Social Internet of Vehicles (SIoV) fields. They also trigger further related research and technology improvements in application of VANETs. Honorably, this special issue serves as a land-mark source for education, information, and reference to professors, researchers, and graduate students interested in updating their knowledge of Cyber Security, cyber-physical-system, Internet of Vehicles, and novel application models for future information services and systems. The special issue of this journal covers different aspects of the problem, from both the theoretical and the practical side.

The guest editors would like to express sincere gratitude to Prof. Ling Liu for giving us the opportunity to prepare this Special Issue. In addition, we are deeply indebted to numerous reviewers for their professional effort, insight, and hard work put into commenting on the selected articles that reflect the essence of this special issue. We are grateful to all authors for their contributions and for undertaking two-cycle revisions of their manuscripts, without which this special issue could not have been produced.