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This paper presents a novel framework for enhancing privacy in Vehicular Ad Hoc Networks (VANETs) by integrating homomorphic encryption with machine learning applications. VANETs, essential for Intelligent Transport Systems (ITS), face significant challenges in privacy and security due to their highly dynamic and heterogeneous nature. Our framework addresses these challenges by employing a simplified but effective machine learning algorithm, the K-nearest neighbors (KNN), to ensure the security and privacy of the network. The flexibility of the framework allows for the incorporation of other machine learning algorithms, enhancing its adaptability and efficiency in various VANET scenarios. Key to this framework is the use of homomorphic encryption (HE), a cryptographic technique that enables computations on encrypted data without the need for decryption. This feature preserves data confidentiality and allows for secure third-party computations. Our paper discusses the evolution and types of homomorphic encryption, emphasizing the importance of Fully Homomorphic Encryption (FHE) for its ability to evaluate complex polynomial functions. The paper also highlights the different domains of cybersecurity concerns in VANETs, including in-vehicle systems, ad-hoc and infrastructure networks, and data analysis. The proposed framework aims to mitigate these vulnerabilities, particularly focusing on preventing common attacks like DoS and location tracking. A significant advantage of our approach is its general nature, making it applicable to various privacy issues in VANETs. We propose the potential integration of homomorphic encryption with other privacy-preserving techniques, such as differential privacy or secure multi-party computation, to enhance computation times while ensuring robust privacy protection. [ABSTRACT FROM AUTHOR]

There has been a significant growth in the number of private automobiles in the previous decade as a result of technological advancements and changes in people's lifestyles. This creates a major problem of congestion main reason being the transportation infrastructure's inadequately construction and maintenance, which is the case in most major cities. By broadcasting the vehicle's current location and surroundings in real time to other adjacent cars and roadside devices, VANETs may help alleviate traffic congestion by using intelligent algorithms to anticipate and avoid potential bottlenecks on the route. This can also be used to predict the shortest path from initial location to destination making it easy for the drivers in the choosing the right path. To implement all these functionalities, we need a robust and secure communication channel and also an intelligent and complex algorithm. Our aim is to predict a lane with least probability of accidents and find the shortest path with least travelling time and congestion. This keeps the user informed regarding the surrounding scenario's and makes more coordinated, safer, and optimal use of transport networks. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of a vehicle fleet is a complex task that can be identified as having multiple variables and nonlinear feedback loops influenced by different mobility, socio-economical, and environmental factors. Due to the prevailing concerns on the fuel economy and vehicular emissions, formulating a clear policy for electric and other alternative mobility forms requires a clear decision support tool to adequately consider the complexities involved. This paper presents a novel System Dynamics (SD) approach based on the cause-and-effect analysis and feedback loop structures as a suitable tool. The proposed SD model comprises four sub-models: socio-economic, new vehicle registrations, environmental influence, and fuel economy and e-mobility policy measures. The study presents the analysis of historical data and results from the impact analysis of fuel economy policies in Sri Lanka, including the introduction of electric mobility. The authors leveraged the Vensim SD software package for modelling, expert-in-the-loop sensitivity analysis and optimization. Three strategic fuel economy and e-mobility policy intervention scenarios have been developed to estimate the forecasted fuel economy for Four-Wheeled Passenger Vehicles (FWPVs) as per strategic interventions to change the fuel type of new vehicles registered. It is shown that hybrid and electric FWPVs can improve fuel economy by between 13 and 39% by 2030. • A System Dynamics approach is proposed to model the energy efficiency of Four-Wheeled Passenger Vehicles' registrations. • Four sub-models are developed to report the impacts of fuel economy and e-mobility policies. • Three policy scenarios have been formulated to test the application of a target of reaching 50% of the conventional fuel registrations. • The effective change of fuel economy improvement under three scenarios vary between 13 to 39%. [ABSTRACT FROM AUTHOR]

The security of ad hoc networks continues to pose a major challenge in today's digital age. Threatened by unscrupulous users, especially in decentralized and open architectures, ad-hoc vehicular networks make protection against malicious attacks a difficult task. Vehicular networks, a specific subset of ad hoc networks, inherit security vulnerabilities from their parent network. One of the best-documented attacks is the black hole, in which a malicious user attempts to manipulate the routing mechanism to gain unauthorized access to the route, then intercepts and discards data packets. The result is an interruption of service to the intended recipient. In our research, we propose a black hole attack detection system based on the Kolmogorov-Smirnov statistical method. This system is designed to identify communication disruptions caused by such attacks without requiring structural modifications to the routing protocol. The results show that the proposed method can detect the presence of the attack by monitoring network activity. The results are tested and validated using SUMO (Simulation of Urban MObility) to generate microscopic road traffic, and NS-2 (Network Simulator) to generate network communication. [ABSTRACT FROM AUTHOR]

The integration of cyber-physical systems (CPS) and Internet of vehicles (IoV) improves the solution for safe driving, traffic reduction, assisted navigation, and secure communication, etc. Based on the communication and service demands, the security and privacy requirements of the users vary. Therefore, unanimous and conventional key-different security methods are required for privacy throughout the driving distance. For strengthening uninterrupted services with precise privacy, this article introduces a diversified context-based privacy-preserving scheme (DCP2S). This scheme considers two contexts, namely navigation support and information communication exchange for balancing two distinct privacy administrations. In the navigation-dependent process, privacy is ensured using two-way authentication, which is verified by the CPS, whereas a one-way authentication is sufficient for information exchange. Both authentication processes are monitored using external CPS with interchangeable security functions for securing consumer-centric communications. This means the security amendments are valid for authentication and privacy preservation regardless of service utilization. The concurrency in employing the security modes is verified using knowledge learning for preventing interrupts. More specifically, knowledge learning accumulates interruptions and failures from the previous information exchange for each of its modes. Based on this information, further authentication from the vehicle is generated; the CPS is responsible for monitoring and information sharing for joint adversary injections. The proposed scheme maximizes service utilization, interrupt detection, and authentication verification by 11.26%, 14.37%, and 7.56% respectively. Also, scheme has achieved lower service failure and authentication time by 8.98% and 445.382 ms, respectively, which is superior to existing techniques. [ABSTRACT FROM AUTHOR]

The modern advancements in mobile and wireless communications have made an tremendous change in human lifestyles. Mobile Ad-hoc Networks (MANETs) are self-organized and self-configured networks consisting of moving nodes that communicate through wireless communication systems. Vehicular Ad-hoc Network (VANET) is a special category of MANETs with some distinctive set of characteristics. Some common features of VANET are its use of multi-hop communications between mobile vehicles with limited bandwidth and self-organization of the moving vehicles. Also, VANET is differed from MANET by the feature of high mobility of nodes. One significant advantage of VANET over MANET is that the computing devices get continuous power from its vehicles. Scalability refers to a VANET's ability to accept a rising number of communicating vehicles without experiencing disruption or loss in data transfer or traffic loading, which increases administrative complexity and reduces network performance. Because of its difficult properties, designing a scalable and robust network is an open topic of research in VANET. When VANETs go from sparse to dense mode, or from high mobility to slow traffic conditions, several design methods fall short. The performance metrics Average Goodput, MAC/PHY overhead, packet received, and receive rate over four VANET routing protocols- Ad-hoc On Demand Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), and Optimized Link State Routing (OLSR) in a defined area. The results showed that DSR performed well in average goodput,MAC/PHY overhead, receive rate, and packets received in low and highly scalable scenarios. [ABSTRACT FROM AUTHOR]

The popularity of vehicles and the upgrading of sensors are driving the development of the Internet of Vehicles. Therefore, a number of Vehicular Adhoc networks (VANETs) routing algorithms have emerged. However, vehicles in complex road environments are exposed to numerous malicious attacks. Especially in multi-hop communications, these malicious attacks become more dangerous. So it remains a great challenge to transmit the packet safely to the destination through multi-hop routing when a vehicle is in the area outside the Road Side Unit (RSU) signal coverage. Considering the process of past communication between the vehicles benefits our assessment of the vehicle's security. In this paper, we propose a secure multi-hop routing algorithm based on accumulating trust. First, we obtain the number of times the vehicle forwards the packets through neighbor nodes. Then, we calculate the current trust value of the vehicle based on it, and RSUs will put the current trust value into the blockchain to form an accumulating trust value. Finally, the appropriate route is selected for communication based on the accumulating trust value of the vehicular nodes. At the end of the paper, we carry out a simple numerical simulation for our routing algorithm. [ABSTRACT FROM AUTHOR]

The study of ad hoc networks and their different varieties, including wireless sensor networks, wireless mesh networks, and mobile ad hoc networks, is discussed in this paper. Wireless devices link to one another directly in an ad hoc network without the use of a central Wireless Access Point (WAP). Data transit between wireless devices is frequently managed and directed by a base station or WAP device. As soon as two or more devices converse with one another, an ad hoc network is instantaneously created. Although placing these devices close to one another is preferred, the network's quality and speed will decline as more devices join it. The idea of a vehicular ad hoc network, an intelligent vehicular ad hoc network, and a mobile ad hoc network is also introduced in ad hoc networks since nodes travel from one point to another. Any node can join and exit this network at any time. Nodes are mobile systems or gadgets that are a part of the network, like a mobile phone, laptop, personal digital assistant, MP3 player, or personal computer. Apart from this, the paper also discusses the most important results from 2000 to 2022 from various authors. In addition, the paper contrasts mobile ad hoc networks, wireless sensor networks, and wireless mesh networks in terms of their uses, limitations, issues, characteristics, and advantages and disadvantages of routing protocols. The most important things for ad hoc networks are that the different types of nodes (end-to-end nodes, intermediate nodes, and wireless antennas) are safe and respond quickly. [ABSTRACT FROM AUTHOR]

Communication and collaboration in transport systems are overdeveloped through Vehicular Ad hoc Networks (VANET). In VANETs, various messages are sent to activate warnings about road accidents, sharp turns, traffic jams, etc. Data security in VANETs is then essential to prevent the intrusion of hackers. Although security and safety in VANETs have been the subject of several research works, some intelligent vehicles contain extremely sensitive data that require specific high-level and real-time security solutions. This is the case for connected medical ambulances in which surgery can be performed with the help of an outside doctor. In such cases, video is the most complicated type of data to handle due to its size, content, and structure. In this paper, an elliptical approach to video representation is introduced, and a video crypto-compression scheme is proposed. The scheme described is built on Elliptic Curve Cryptography, which demonstrates good performance in computer security in recent years. [ABSTRACT FROM AUTHOR]

Road accidents and traffic congestion severely impact road safety, resulting in significant loss of life and property. Therefore, there is a need for road safety and traffic management. The wireless networks of connected vehicles called Vehicular Adhoc Networks (VANETs) are designed to improve the traffic management on highways with comfort and safety for drivers and passengers. However, there is a need to develop an authentication scheme that can protect the privacy of vehicles and secure them against attacks such as insider attacks and repudiation attacks. In this article, we present a scheme that performs vehicle registration and message forwarding in the VANETs and appends the transactions to the blockchain. The proposed privacy-preserving authentication scheme using blockchain technology enables vehicles and Trusted Authorities (TA) to authenticate vehicles while preserving privacy. The proposed scheme facilitates privacy-preserving authentication by deploying the scheme on a decentralized network. The proposed scheme ensures privacy, confidentiality, integrity, and transparency during vehicle authentication for VANETs. In the proposed scheme, an authentication process is automated with the use of a smart contract. We comparatively evaluate the security and performance of the proposed privacy-preserving authentication scheme with the state-of-the-art literature. The proposed privacy-preserving authentication scheme is significantly reliable and transparent in authenticating vehicles while preserving privacy. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence. [ABSTRACT FROM AUTHOR]

Ensuring secure authentication between participating entities in VANETs has emerged as a critical challenge. Most of existing schemes mainly consider authentication issue in single administrative domain and suffer from various limitations that include privacy-preserving and malicious entity tracking. This paper proposes a double-layer blockchain-assisted conditional privacy-preserving cross-domain authentication scheme (DBCPCA) that leverages blockchain technology and certificate-less signatures to address these challenges. In DBCPCA, the upper-layer blockchain is used in cross-domain authentication by sharing inter-domain information among multiple different administrative domains. The lower-layer blockchain is employed in intra-domain authentication. In DBCPCA, we also introduce an anonymity mechanism to protect the real identity of a vehicle while enabling the system to trace a malicious vehicle, thereby addressing conditional privacy-preserving concerns. In addition, a security analysis of the proposed scheme demonstrates that it can meet our specified security objectives. Finally, we make a detailed experimental comparison with the most relative solutions such as BCPPA and BCGS. The results show that the DBCPCA scheme reduces the time cost by at least 66.68 % compared to the BCPPA scheme during the signature generation phase. During the signature verification phase, the DBCPCA scheme reduces the time cost by at least 62.39 % compared to the BCGS scheme. [ABSTRACT FROM AUTHOR]

A Roadside Unit (RSU) serves as essential infrastructure in Vehicular Ad Hoc Networks (VANETs) that supports the goals of Intelligent Transportation Systems (ITS) by providing safety services, shared storage, and enhanced internet connectivity to vehicular users, drivers, and pedestrians. Additionally, the efficiency of VANETs, concerning network service utility and latency, depends on the relative positioning of these RSUs within the network topology. Most existing RSU deployment approaches deal with a single objective, either enhancing network service utility or minimizing the latency. For instance, some studies suggest deploying RSUs in high-traffic road segments that enhance network service utility but lead to higher latency. Conversely, some suggest deploying the RSUs in low-traffic road segments that minimize the network latency, but there will be low network service utility. Hence, there exists a trade-off between these two conflicting objectives in VANETs, and none of the studies address both objectives simultaneously. To achieve the balance between these two objectives, this paper proposes a Multi-Objective UAV assisted RSU Deployment (MOURD) scheme that leverages the Unmanned Aerial Vehicles (UAVs) for VANET efficiency. The MOURD scheme statically places RSUs in high-traffic road segments and dynamically dispatches the UAVs in low-traffic road segments to facilitate seamless network coverage and minimize the overall network latency. The simulation results on the road network of Delhi, India, demonstrate the effectiveness of the proposed MOURD scheme compared to other benchmark RSU & UAV deployment approaches. MOURD scheme outperforms on an average of 17.42%, 13.29%, 15.67% and 6.23% in terms of vehicle connection time, packet delivery ratio, throughput, and latency, respectively. [ABSTRACT FROM AUTHOR]

A Space-Air-Ground Integrated Network (SAGIN) is a novel networking concept that integrates satellite networks, aerial networks and terrestrial networks into a 3-tiered network. It has been developed as an adaptable computing and traffic model in the present decade. In addition to various benefits, there are some unprecedented challenges in SAGIN, and reliability is one among them. The wireless communication network requires stable communication between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). For ensuring reliable communication links, Massive Multiple-Input Multiple-Output (MaMIMO) is used with the deployed aerial vehicles, and the mobility of UGVs can be controlled by UAV, providing device-to-device (D2D) communication between vehicular nodes, so that no interruption can occur. This work involves developing a 3-Tier D2D Architecture, comprised of network links UGVs, network links UAVs, and the combined model of both and also links of the satellite groups at lower orbits. The UAV senses the environment and transmits the data to its operator for appropriate decision-making. This work focuses on incorporating D2D communications in SAGIN for ensuring reliability via Reinforcement Learning (RL) based on Markov process. We derive the optimal numbers of transmitting nodes used for communication links, represent the Markov State transition and provide the Bellman equation for our model. The main objective is to maximize Energy Efficiency (EE), under the limitations of Spectral Efficiency (SE). Performance evaluations are employed to assess reliability over the links in the corresponding proposed architecture. The simulation results with water-filling optimization show that the proposed model achieves enhanced EE and SE per D2D link. [ABSTRACT FROM AUTHOR]

The internet of vehicles technology provides convenience to drivers and prevents traffic accidents via wireless communication between road infrastructure and autonomous vehicles by sharing real-time traffic information. However, attackers can easily penetrate networks by exploiting the vulnerabilities of wireless communications. An attacker can falsify real-time traffic information and transmit it to a vehicle, causing traffic jams or preventing autonomous vehicles from receiving legitimate real-time traffic information. If autonomous vehicles do not receive accurate information, the arrival time at the destination can be affected, and accidents due to incorrect driving can occur. Because traffic accidents can cause casualties, they must be prevented. Various schemes have been proposed to detect attacks that occur on the internet of vehicles, and these security schemes can prevent traffic accidents by detecting attacks at high speeds. However, the existing schemes focus on quickly identifying a single attack but encounter difficulties when attempting to detect complex attacks that occur simultaneously. The proposed scheme uses a history trajectory to detect complex attacks. The proposed scheme stores behavioral information on all vehicles and road infrastructure using a control center. This information becomes a history trajectory that is used to detect attacks. Thereafter, when the vehicle is abnormally driven, the control center analyzes its driving path. When analyzing the vehicle driving process, the control center determines that an attack is being attempted when the road infrastructure or a vehicle makes an erroneous state transition. In addition, the type of attack is analyzed to identify compromised vehicles or road infrastructure and take measures to prevent further problems. Thus, the proposed scheme can detect complex attacks through history trajectory analysis. The experimental results demonstrate that in 80% of attempted attacks, the proposed scheme detects complex attacks with a probability of 97.56%. [ABSTRACT FROM AUTHOR]

Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities. The proposed system provides innovative services to the drivers that enable a view of traffic flow and the volume of vehicles available on the road remotely, intending to avoid traffic jams. The proposed model improves traffic flow and decreases congestion. The proposed system provides an accuracy of 95% and a miss rate of 5%, which is better than previous approaches. [ABSTRACT FROM AUTHOR]

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission. [ABSTRACT FROM AUTHOR]

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models. [ABSTRACT FROM AUTHOR]

Recently, Gong et al. proposed a Certificateless Aggregate Signature (CLAS) scheme (shorted by PCAS) with conditional privacy-preserving without bilinear pairing for Vehicular Ad-hoc Networks (VANETs). Unfortunately, after analyzing security, the PCAS scheme fails to satisfy the necessary unlinkability in VANETs and is vulnerable to the public key replacement forgery attack of the Type-I adversary. Subsequently, this article analyzes the causes of these two security issues in detail and proposes an improved CLAS scheme for VANETs (called IPCAS). Through security analysis, this article proves that the IPCAS is existentially unforgeable under the adaptive chosen message attacks against Type-I and Type-II adversaries in the random oracle model, and satisfies the necessary security and privacy requirements in VANETs, including unlinkability. Finally, the performance analysis results show that compared to PCAS for single message and n aggregate messages (n = 1000) , the computational overhead of IPCAS is reduced by 20.01% and 49.08% respectively, and the communication overhead is reduced by 18.75% and 28.14% respectively. Therefore, IPCAS not only makes up for the security vulnerabilities of PCAS but also has better performance. [ABSTRACT FROM AUTHOR]

Mobile data offloading is a highly promising approach in mobile networks that tackles network congestion at Base Stations (BSs) and greatly improves both the Quality of Service (QoS) and Quality of Experience (QoE) for users. It presents significant business opportunities for operators, particularly in light of the exponential growth in mobile data traffic and the ongoing digital transformation. To effectively uphold the desired levels of QoS and QoE in the elevation of escalating digitalization and the unprecedented surge in data traffic, this paper presents offloading through a diverse range of technologies such as data offloading through Small Cell Networks (SCNs), Wi-Fi offloading, Device-to-Device (D2D) offloading, and data offloading through Vehicular Ad-Hoc Networks (VANETs). The SCNs and Wi-Fi offloading involve migrating data traffic to the alternative infrastructure i.e. the small BS and the Wi-Fi Access Points (AP), respectively while D2D focuses on transferring data through the device without transversing the BSs. VANETs is the process of offloading data in vehicular scenarios that consist of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). Additionally, mobile data offloading from cellular BS is categorized into four main factors: energy consumption or energy awareness, economic considerations, user satisfaction, and network congestion. These factors play a crucial role in the ongoing adoption and implementation of mobile data offloading strategies. Different technologies utilize diverse techniques to tackle the challenge of offloading, aligning with their specific research objectives. This paper delves into the challenges and outlines future research directions in the field of mobile traffic offloading. [ABSTRACT FROM AUTHOR]

Integrated sensing and communications (ISAC) is one of the key technologies of 6th generation mobile communication (6G). In vehicle edge computing (VEC) networks, ISAC can assist vehicles in real-time sensing of the surrounding environment and establish a robust shared sensing network. However, the existing VEC offloading method cannot handle the large amount of computational data generated by joint sensing computation while meeting the task delay and energy consumption requirements. Therefore, in order to solve the above problems, we propose a Vehicle-Assisted Fusion Perception Offloading in vehicular edge computing network (VAFPO) scheme, which utilizes the perception characteristics of ISAC, and the task vehicle adaptively uploads data or instructions to the auxiliary node. Adaptive uploading of data or instructions to the auxiliary node, the auxiliary node uses its own perceived data to calculate when transmitting instructions, at the same time, the scheme constructs a delay and energy consumption priority factor according to the task characteristics to get the delay and energy consumption priority, in order to minimize the total system overhead. Finally, we propose a State Normalized DDPG for Adaptive Offloading (SNDAO) algorithm, which increases the convergence speed of the algorithm to obtain the optimal offloading decision by normalizing the states obtained from the agent's interaction with the environment, and at the same time adopting an actor-critic network with an experience playback mechanism. Simulation results demonstrate the effectiveness of the SNDAO algorithm and verify that the VAFPO scheme is significantly better than the current benchmark scheme. [ABSTRACT FROM AUTHOR]

The traditional Internet architecture provides applications with a stable end-to-end connection between a requester and a source node holding the information. However, in a vehicular environment, this connection is not stable due to the mobility of vehicles that results in location change, from the requester, the source, or both. To overcome these situations, Named Data Networking (NDN) has been proposed as new architecture for data retrieval and mobility support. This new paradigm enhances content access and dissemination by decoupling the content from its original location. In this paper, we introduce the forwarding in NDN-based VANETs and highlight its benefits and limitations. We propose a classification of NDN-based VANETs forwarding strategies, then detail the representative schemes. Furthermore, we provide a review and comparison of existing forwarding strategies in terms of various attributes such as transmission mode, forwarding strategies, changes of NDN architecture, application scenarios, problem addressed, evaluation metrics, simulation platform. Finally, we conclude our contribution by identifying main open research challenges that can be exploited for future works. We believe that this survey will help NDN-based VANETs researchers community to easily understand the forwarding in vehicular environment, overcome some repeated solutions and gives further inspiration to design a new protocols to improve NDN-based VANETs networks in this context for further relevant research works. [ABSTRACT FROM AUTHOR]

The location verification of the vehicles interacting during the process of communication needs to be cooperatively determined under Non Line Of Sight (NLOS) situations for facilitating risk free environment with the least degree of congestion. The vehicular nodes in NLOS conditions possess the possibility of introducing channel congestion and broadcast storm into the network either intentionally or unintentionally during the event of emergency message dissemination in the vehicular network. Thus, the NLOS vehicular nodes of the network need to be detected through direct interaction and neighbourhood collaboration such that the rate of emergency message dissemination is sustained to the maximum degree. In this paper, a Rank Criteria Improved Confidence-based Centroid Scheme (RCICCS) is proposed for potential localization of NLOS nodes. This proposed RCICCS scheme uses an integrated cost, computed using the primitive cost and penalty cost in order to increase the effectiveness in localizing NLOS nodes. Thus effective location of NLOS nodes is determined based on rank criterion-based neighbor confidence measure that are iteratively enhanced during the process of perturbation using the method of gradients. The experimental analysis of the proposed RCICCS scheme confirmed a remarkable enhancement rate of emergency message delivery and neighbourhood awareness under NLOS conditions. [ABSTRACT FROM AUTHOR]

Alert message (AM) dissemination is a fundamental yet challenging issue in vehicular networks, as it relies on wireless transmissions in a highly mobile, potentially dense, and changing environment. Emerging network infrastructure-based vehicular networks are being considered as an alternative to Vehicular Ad-hoc NETworks (VANETs) for alert message dissemination. Indeed, assuming that Vehicle to Infrastructure (V2I) communication links are nominally available, with some transient and time-limited connectivity losses, recent alert message dissemination schemes primarily rely on V2I links to widely broadcast AMs. Vehicle to Vehicle (V2V) rebroadcasts performed by some selected relay vehicles located within pre-computed rebroadcast zones are then used to ensure the full dissemination within an area of interest. This paper focuses on rebroadcast zones placement. It proposes a Q-learning-based method that computes the minimum number and optimal locations of rebroadcast zones. From these computed zones, the combination of V2I broadcasts with V2V rebroadcasts allows the delivery of AMs in a whole area, even in the presence of locations with poor wireless connectivity. The performance results show that high information coverage and low delivery delays are achieved with our proposed Q-learning based placement. Useless duplicate rebroadcasts and collisions are also avoided, saving network resources. [ABSTRACT FROM AUTHOR]

Anonymous authentication is an important mechanism for providing security and privacy for vehicles in the increasing development of Internet of Vehicles (IoV). Most of the existing authentication mechanisms depend on a central trusted third party to manage large numbers of certificates or pseudonyms, to conduct the time-consuming database query operations in order to achieve both security and privacy protection for IoV. The huge computation, storage and network bandwidth burden on the central trusted entity affects greatly the performance of the authentication and also leaves potential perils for the whole system. In this paper, we suggest a new authentication scheme for IoV. Distributed authentication is achieved by using blockchain technique, in which large numbers of certificates, public-private key pairs or pseudonyms do not need to be stored in vehicles or central trusted entity. The test results and performance analysis show that the proposed scheme can well satisfy the performance requirement of authentication for IoV while preserving privacy for vehicles. [ABSTRACT FROM AUTHOR]

Information and communication technologies applied to means of transport allow the design and development of new applications and bring new solutions for the mobility of people. All these applications are grouped within the field of Intelligent Transport Systems (ITS) and improve user mobility in terms of safety, reliability, capacity, and quality. Vehicle to Everything is a novel technology of ITS which aims to enhance the security and efficiency of road traffic by using wireless communications. Cellular communications networks such as LTE-V2X and 5G NR, integrated into the Vehicle to Everything (V2X) architecture designed to support vehicular communications and deployed on a large scale, appear in particular to be a relevant solution. They could indeed guarantee reliable geographic distribution and acceptable performance (latency, bandwidth, packet loss). In this context, we propose a secure authentication scheme for 5G-based V2X communication. Our solution aims to guarantee a high level of security in different types of vehicular communication (V2V, V2I, V2N) to achieve the security requirements using lightweight cryptographic methods and to support vehicles to receive securely all keys and messages from RSU, other vehicles, or network. To validate our proposal, we utilize the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to achieve the security goals, also we evaluate the performance according to the operational cost which demonstrates that our model has a less computational cost. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. [ABSTRACT FROM AUTHOR]

• Emotions in safety messaging have a significant impact on self-reported driving behavior. • Messages mentioning cell phone use show high recall. • Positively themed messages were recalled more often than others. • Consequences had no significant effect on self-reported driving behavior. In recent years numerous transportation agencies have begun displaying creative safety messages like "get your head out of your apps" on dynamic message signs (DMS). DMS have been used traditionally to provide real-time information about travel times, weather conditions, construction, and incidents. Applying emotional and consequential concepts from protection motivation theory, which has a track record of leading to behavioral changes from public awareness campaigns, this study evaluates how the use of safety messages on DMS impacts driver perceptions. We find that emotions, but not consequences, impact self-reported driving behavior. Further, while drivers reported negative emotions would change their driving habits, positive messages were recalled far more often. Cell phone related messages were recalled most frequently despite being rarely displayed along roadways in Michigan where the study is conducted. Thus, the results support the use of positively themed messages with consequences that drivers perceive as realistic. [ABSTRACT FROM AUTHOR]

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. [ABSTRACT FROM AUTHOR]

This paper investigates the vehicle classification (VC) based on vehicular ad-hoc networks (VANETs). Using VANETs, one can extract the physical and mobility characteristics of the vehicles globally and in a real-time manner. In this paper, we propose an in-depth novel safety-driven VC method for heterogeneous connected vehicles. In this innovative approach, road vehicles are classified into a broad range of classes according to their distinctive behaviors and safety measures. The proposed method can play a vital role in reducing collisions and can be used as a safety standard reference in VANETs-based VC systems. Furthermore, advance driver assistance systems (ADAS) can integrate this method and extend road safety by notifying vehicles of dangerous situations on the road using V2X communication. [ABSTRACT FROM AUTHOR]

• Evaluating different models' perspectives for real-time adversarial attack detection in VANETs. • Identifying and preprocessing DSRC adversarial data using standard and fusion preprocessing approaches. • Selection for the golden ML model using a fuzzy decision-making framework for VANETs. • Evaluating the efficiency of the developed framework in four assessment processes. This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective. [ABSTRACT FROM AUTHOR]

The Fifth Generation (5 G) networks exhibit high flexibility and diversity in their design and deployment strategies. Transitions between base stations (BSs), heterogeneous networks (HetNets) and other cellular networks provide significant vulnerabilities and expose users to substantial risks associated with cybersecurity attacks. This article evaluates current handover authentication methods in the context of 5 G networks while proposing a set of security criteria for handover authentication. This study presents a novel authentication technique called SHK (Secure Handover Key) that utilizes SDNs (Software Defined networks). The proposed scheme integrates recycled lightweight dynamic key cryptography and combines security features such as perfect forward secrecy and robustness to leakages. The significance of the proposed approach is assessed in terms of computations, communications, signals, and energy costs on 5 G mobility applications and Vehicular Communication Networks (VCNs). The scheme employed in this study demonstrates enhanced security measures and improved changeover performance compared to conventional schemes. [ABSTRACT FROM AUTHOR]

Several intelligent transport systems in vehicular networks (VANETs) rely on IP-based models for inter-vehicle communication. However, many approaches for Cooperative Intelligent Transport Systems and distributed systems are not secure and appropriate for VANETs, using specific technologies, centralized network architectures, or limited communication models. In addition, many applications are forced to deal with complex services that the network layer does not provide. This paper proposes NDN-Waze, a comprehensive distributed and lightweight system for vehicular traffic management using Named Data Networking as the main underlying communication protocol. NDN-Waze is a proof-of-concept design that has applications in vehicular route guidance, traffic and mobility monitoring, as well as roadside units as a service in urban areas. We use the NDN-Waze route guidance module to compare the VANET model via Named Data Networking (NDN) and Internet Protocol (IP). Our study employs traditional network metrics to support the hypothesis that the communication architecture has a direct impact on both efficiency and the quality of service operations. The simulation results demonstrate that NDN properties can override traditional IP-based forwarding proposals in VANET. [ABSTRACT FROM AUTHOR]

Learning fine-grained discriminative information is essential to address the challenges of small inter-class differences and large intra-class differences in vehicle re-identification (Re-ID). Attentional mechanism is often used to capture important global information in images rather than fine-grained discriminative information. Studies have shown that the multi-axis interaction of information can enhance the feature representation ability of networks. This paper explores how to use the multi-axis interaction of information to facilitate more effective learning of attention and how to capture important detailed information in local regions. We propose a multi-axis interactive multidimensional attention network (MIMA-Net) for vehicle Re-ID. The network allows information to interact on multiple axes and calibrates the weight distribution of features from multiple dimensions to learn subtle discriminative information in vehicle parts/regions. The window-channel attention module (W-CAM) in MIMA-Net facilitates the learning of channel attention by interacting first across locations and then across channels, while the channel group-spatial attention module (CG-SAM) facilitates the learning of spatial attention by interacting first across channels and then across locations. These two modules perform window partitioning in a priori manner and channel semantic aggregation in an adaptive manner to learn discriminative semantic features in parts, respectively. These two approaches complement each other to strengthen the feature representation ability of MIMA-Net. Extensive experiments on three large public datasets, VeRi-776, VehicleID, and VERI-Wild, verify the effectiveness of our MIMA-Net and show that our method achieves state-of-the-art performance. • Utilizing multi-axis interaction of information to promote attention learning. • Learning subtle semantics via multidimensional calibration of weights. • A priori and adaptive ways are combined to learn part-level features. [ABSTRACT FROM AUTHOR]

The popularity of intelligent vehicles with cutting-edge vehicular applications has fueled the rapid expansion of Vehicular Ad hoc Networks (VANETs) in recent years. VANETs are a network of vehicles designed to exchange and explore real-time data using a well-developed and effectively organized data transport technology. However, the major issue of dynamic topology and cluster stability always has an impact on choosing an optimal path between the cars. At this point, an intelligent clustering technique in VANETs that handles dynamic topology and cluster stability is critical for efficient route selection between vehicular nodes. This is an NP-hard issue that can be effectively solved using an intelligent nature-inspired algorithm that can discover near-optimal solutions in the search space. An Intelligent Hybrid Fennec Fox and Sand Cat Optimization Algorithm (HFFSCOA) -Based Clustering Scheme is proposed in this paper as a novel route clustering optimization strategy that takes grid size, orientation, velocity node density, and communication range into account while achieving its goal. This HFFSCOA contributed to the route clustering process, which determines dependable and optimal routes between vehicular nodes for the purpose of building and evaluating ideal Cluster Heads (CHs) in the network. HFFSCOA's findings clearly demonstrated its usefulness and efficacy in terms of the number of vehicles, network size, changeable communication ranges, and number of clusters built in the network. The statistical results of HFFSCOA also confirmed an enhanced cluster Optimization rate of 56.21% and an increased cluster stability of 92.34. • The design blends local and global search rates in the search space by combining FFOA and improving SCOA. • The goal is to reduce the establishment of clusters, increasing the network's stability and scalability. • Introduces a new fitness function that only uses vehicles with the highest potential to act as CHs inside the network. • Finding best routes between source and target vehicles using link stability, energy, and transmission range. [ABSTRACT FROM AUTHOR]

In Vehicular ad hoc network (VANETs), Quality of Service (QoS)- aware protocols helps in handling the necessitated demand of delay sensitive applications for facilitating intelligent transportation. The fundamental challenge of VANETs lies in the process of establishing vehicle to infrastructure and vehicle-to-vehicle communication that are prone to link failure. Bio-inspired algorithms are identified to provide reliable solutions for securing the links of VANETs. In this paper, Komodo Mlipir Algorithm-based Optimal Route Determination Mechanism (KMAORDM) is proposed for achieving best optimal route that guarantees enhanced QoS. This QoS aware routing protocol utilizes the exceptional potentiality of Komodo Mlipir Algorithm attributed in terms of exploration and exploitation for the objective of evaluating and memorizing the impactful factors that helps in exchanging the necessitated messages to its neighbours. It adopts Mlipir as a best mode of modelling the behaviours of vehicular nodes during the process of data routing in VANETs. It uses a cache strategy which discovered reliable traversed routing paths such that the pre-cached route is used for data transmission. This cache strategy paves the way for preventing the route identification and maintenance process with minimized routing overhead. It also identifies the fittest vehicular node from its one hop distance as the successive forwarder in the absence of pre-cached route for addressing link failure during reliable packet transmission. The simulation results of this proposed KMAORDM approach with different vehicular nodes confirmed minimized communication overhead of 19.32 %, reduced network latency of 18.64 %, maximized throughput of 18.54 % better than benchmarked approaches. • Exploration and exploitation of Komodo Mlipir-Based Route-Finding Algorithm determines ideal route between vehicles. • Urban VANET communication overhead is reduced by cache memory-assisted RSUs' reliable backhaul link with neighbouring RSUs. • KMRFA algorithm balances exploration and exploitation, so nearby RSUs use past route info if vehicle clusters move uniformly. • Limiting control packet broadcast reduces discovery time and link failure when previous route is not in RSU cache memory. [ABSTRACT FROM AUTHOR]

In a vehicular environment, by becoming connected, vehicles are subject to more threats in comparison to traditional information systems, with the difference that, as a cyber-physical system, anomalies and intrusions could have repercussions in the physical world. In this work, we have developed an ontological anomaly-detection approach (OADA). The anomalies studied in this work mainly concern: network scans, DNS tunnel attacks, and telemetry data anomalies. Our contribution relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic, such as flow or latency. They are subject to the process of detecting anomalies and intrusions, carried out thanks to the algorithm with HTM. For each entry, the algorithm produces a score that allows us to decide if a window is abnormal and to lift an alert if that is the case. We evaluated our system using a communications and anomalies emulation tool. We use the corpus of data produced thanks to Autobot. We seek to determine from among the best scores of Matthews correlation coefficient (MCC) and Detection efficiency score (DES) which were the parameters for which HTM detects all anomalies with the greatest possible coverage. The obtained results prove that HTM can detect all anomalies for each window duration. [ABSTRACT FROM AUTHOR]

Nowadays, a numerous fatalities have resulted from road damage. Research into road damage detection, particularly the detection and notification of hazardous road damage is essential for enhancing the traffic security. Existing road damage identification schemes often process data in the cloud, but they are unable to warn users on time on account of lengthy latency. Although newer edge computing strategies reduce this issue owing to the limited communication range of edges, the users can only get notifications about potentially harmful road damage within a small radius. Besides, untrusted nodes may misuse the sensitive information of users. Therefore, in this manuscript, a Privacy-preserving Dual Interactive Wasserstein Generative Adversarial Network is proposed for Cloud-Based Road Condition Monitoring in VANETs (PP-DIWGAN-C-RCM-VANET). Here, local models and global models are the two types of cloud detection models are considered. In which, the local models acquire knowledge from cloud-based data, whereas global models support local models' learning by combining local models. The intention of the proposed approach is "to detect the smart hazardous road condition". The local model recognizes dangerous roads, and also categorized into 3 levels: low, middle, and high, these are based on information gathered about the state of the roads. From the road crack dataset, it is categorized into crack, normal and pothole. DIWGAN secures secrecy from unreliable clouds, but it is still significant danger of privacy leaking from unreliable edges. To ensure privacy, users must erase all data before transmitting it to the edges, here the user indicates the owner of the vehicle. Therefore, a new privacy preserving method called Fractional Discrete Meixner Moments Encryption (FDMME) is taken into account to fulfill this requirement. For real time dataset, the proposed method achieves better accuracy 27.5%, 10.32% and 16.65%, better f-score 30.93%, 11.14% and 15.3% compared with existing methods. • The identification and notification of risky road damage, is essential to enhance traffic security. • Local models and global models are the two types of cloud detection models. • The local model recognizes hazardous roads, and also categorizes as 3 levels: low, middle, and high. • DIWGAN secures secrecy from unreliable clouds. • The simulation of the proposed method is activated in python. [ABSTRACT FROM AUTHOR]

With all the advancements in the last decade in Intelligent Transportation systems (ITS), there has been an exponential increase in the number of applications provided to the Vehicular Ad Hoc Networks (VANETs). These applications provide unmatched security and safety services. As a result, there is a huge surge in the amount of data being generated and transmitted within the VANETs. In this paper, we have proposed a dual blockchain based decentralized architecture for authentication of the vehicles and secure & efficient communication within the network. The use of individual blockchains for authentication and message sharing makes the network more efficient and fast while also helping to increase the network security. Both the blockchains use a different consensus algorithm which adds another layer of security. The security and performance evaluation of the proposed architecture proves that it is resistant to a number of security attacks and has a lower computational cost, higher throughput, lower transmission delay and higher vehicle verification rate by a significant amount when compared to existing schemes. • A dual blockchain framework for authentication and message transmission in VANET. • Decentralized and secured storage of vehicle registration records. • An algorithm for secure message transmission and storage. • D-BLAC Proof-of-Authority (PoA) and Proof-of-Position (PoP) consensus algorithms for the security of the network. • Has higher throughput & verification rate and lower computational cost & delay. [ABSTRACT FROM AUTHOR]

In complex traffic environments, the preceding vehicle in the platoon often experiences frequent acceleration and deceleration due to changing traffic conditions, resulting in fluctuations in the velocity of the following vehicles. While the current spacing strategy can help the platoon regain a stable state during the fluctuation of vehicle flow, it does not effectively suppress the velocity and acceleration fluctuations of the following vehicles during the adjustment process. This paper proposes a novel variable time headway (VTH) spacing strategy, which employs the neighboring vehicle velocity ratio to depict the relative variations in velocity and considers the impact of the preceding vehicle's acceleration. As a result, the fluctuations of the preceding vehicle can be precisely characterized allowing the following vehicles to respond promptly and accurately to the changes in the behavior of the preceding vehicle. Simultaneously, a robust low communication dependency (RLCD) system based on the third-order dynamic model is proposed to mitigate the impact of complexity and reliability of information flow topology (IFT) on platoon stability in the Vehicular Ad hoc NETwork (VANET). Based on the proposed VTH strategy, a robust coupled sliding mode controller for the platoon is designed under the low communication dependency requirement. Furthermore, by introducing dynamic sliding mode control into the coupled sliding mode surface, a smoother controller input is obtained while ensuring the string stability of the vehicle platoon. The numerical simulation results demonstrate conclusively that the proposed strategy effectively reduces velocity tracking errors and acceleration fluctuations for connected vehicles (CVs) in complex traffic environments. The fluctuations of the preceding vehicles can be more effectively absorbed, and the overall tracking performance of the vehicle platoon is enhanced. • A novel variable time headway strategy is proposed to reduce acceleration fluctuations caused by the preceding vehicle. • A robust low communication dependency system is designed to mitigate the impact of communication unreliability. • Smoother controller input is obtained by introducing dynamic sliding mode control into the coupled sliding mode surface. • Simulation results prove that the proposed spacing strategy has superior performance compared to other spacing strategies. [ABSTRACT FROM AUTHOR]

• Vehicle ad hoc networks (VANETs) are gaining popularity because they have the potential to increase traffic efficiency with road safety in cooperative intelligent transport systems (C-ITS). Effective vehicle deployment requires the identification of nodes providing false data. Researchers have focused a lot of their attention on an important issue called misbehaviour detection for disseminating false data about vehicle information (position, time, speed and vehicle id) in VANETs. • This may endanger the network's safety & confidentiality even result in a number of issues, such as collisions, disasters, & traffic congestion. Additional projects centred on methods of machine learning have been presented. • But all of the machine learning-based identification techniques in use today are signature-based, needing both a large number of characteristics with previous understanding of the assaults to recognise them, thereby raising the computing time required in identifying assaults. • In this method, we propose a creative approach to recognizing the position falsification attacks in VANETs based on a novel deep-learning model with the strongest feature selection utilizing a marine predator algorithm based on teaching-learning. We evaluate our method using a publicly accessible dataset of VeReMi, and we demonstrate that it achieves high detection accuracy for a variety of attacks. • According to our findings, our method can serve as a useful tool for enhancing VANET security and assuring the reliable and secure functioning of vehicular communications. Vehicle ad hoc networks (VANETs) are gaining popularity because they can potentially increase traffic efficiency with road safety in cooperative intelligent transport systems (C-ITS). Effective vehicle deployment requires the identification of nodes providing false data. Researchers have focused much of their attention on an important issue called misbehaviour detection for disseminating false data about vehicle information (position, time, speed and vehicle id) in VANETs. It can threaten the security and privacy of the network and cause some problems, including accidents, collisions, and traffic congestion. There are more works have been proposed based on machine learning methods. However, all of the machine learning-based detection models used currently are signature-based, necessitating previous knowledge of the attacks to identify them and also requiring numerous features, which increases the computational time needed to detect attacks and to select the high-performing features; this works a marine predator algorithm based on teaching-learning. The article provides a unique deep learning strategy to recognize the position falsification attacks in VANETs to find hidden patterns from the data for fighting against known and emerging threats. We evaluate our method using a publicly accessible VeReMi dataset, demonstrating that it achieves high detection accuracy of 99.55% for various attacks. According to our findings, our method can be useful for enhancing VANET security and assuring vehicular communications' reliable and secure functioning. [ABSTRACT FROM AUTHOR]

The security of data transmission is one of the focus issues in vehicular ad hoc networks (VANETs). The timely and effective dissemination of emergency messages can help drivers make intelligent decisions and improve road safety and traffic efficiency. However, malicious nodes in the network seriously interfere with the proper judgement of vehicles via data tampering or spreading false messages, which brings significant risks to traffic safety. In order to guarantee the security and reliability of data transmission, this paper proposes a hybrid-trust-based emergency message dissemination (HTEMD) model that effectively integrates entity trust into the data consistency evaluation. In the proposed model, for emergency messages from different sources, the receiver first calculates the probability of the event based on the sender's entity trust and event status. Then, it uses the principle of information entropy to conduct consistent analysis of the results to help the vehicle make correct decisions. Afterwards, a series of simulations are conducted in four typical scenarios. The results show that the HTEMD model has strong environmental adaptability and robustness. Moreover, compared with other classical message dissemination models, the correct decision rate of the HTEMD model always remains above 80%, while other models drop to 75.5%, 63.2%, and 40.2%. The model proposed in this paper significantly outperforms the baseline model with a higher correct decision rate and resistance to attack. [ABSTRACT FROM AUTHOR]

Routing in vehicular ad hoc networks (VANETs) enables efficient communication and data exchange between vehicles and infrastructure components, which is a critical aspect of providing intelligent transportation systems (ITS). Due to the dynamic nature of VANETs, where vehicles have high mobility and frequent link disconnections, secure routing protocols are essential for establishing reliable and efficient communication paths. Employing trust criteria in routing is an effective solution that routing protocols use to become secure. In this paper, we propose an RSU-assisted trust-based routing protocol for VANETs (RTRV), which considers trust criteria to provide secure routing. To identify and limit malicious nodes from disrupting the routing, RTRV provides a more reliable monitoring process for trust management, which increases resistance to trust-based attacks. In the monitoring process, as each data packet is sent to a next-hop, two vehicles observe the behavior of the next-hop in forwarding the packet, and the observations are then used to update the direct trust of the next-hop. The observations are additionally reported to RSUs, which gather the reports to update the indirect trust of the next-hop and provide recommendations to vehicles within their transmission range. In addition to indirect trust management, in RTRV, RSUs play an active role in the routing of data packets, which improves the routing performance. We simulated the proposed protocol in different scenarios with SUMO and OMNeT++ tools and then compared it with the TGRV protocol. The results of our analysis conclusively show that RTRV outperforms TGRV in terms of packet delivery ratio, end-to-end delay, and average hop count in both scenarios, with and without RSU. Specifically, in the RTRV without RSU scenario, when the percentage of malicious vehicles reaches 25%, RTRV achieves a 3.8% higher packet delivery ratio compared to TGRV. Furthermore, RTRV demonstrates significantly reduced end-to-end delay and average hop count, with values 0.14 and 0.11 times lower than TGRV, respectively. In addition, the cost and security analysis demonstrate that the proposed protocol has acceptable overheads in memory, communication, and computation and is also resistant to critical trust-based attacks. [ABSTRACT FROM AUTHOR]

Recently, Vehicular Ad hoc Networks (VANETs) have gained extensive attention in both academia and industry. VANETs play a seminal role in smart transportation by enhancing driving convenience and traffic efficiency through real-time information interaction. Despite this, ensuring secure authentication and privacy preservation remains two challenging issues in VANETs. The existence of security schemes that are vulnerable to privacy and security issues or have substantial computation and communication overheads shows the necessity for further research in this area. Therefore, in this article, we present an anonymous authentication scheme based on Zero-Knowledge Proof. To be more specific, we adopted Simulation Extractable Zero-Knowledge SNARKs (SE zk-SNARKs) to achieve anonymity and conditional privacy. Although zk-SNARK proofs are succinct and fast to verify, the time required for proof generation poses a major obstacle in using zk-SNARKs in a time-sensitive VANET environment. To overcome this drawback, we focus on key components of the proof statement, separating the repetitive calculations, and archiving in the Blockchain, which serves as an immutable data storage. By omitting repetitive calculations, our proposed scheme becomes lightweight enough to run efficiently on VANETs. Security analysis showed that the proposed protocol satisfied security and privacy requirements. Additionally, a computation cost analysis is provided to demonstrate the significant advantage of our proposed scheme. ProVerif is used to verify the strong secrecy of the protocol, and the results show that privacy can be guaranteed in the proposed scheme. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) is considered as the potential network for implementing risk-free environment that facilitates least cluttered and safer interaction between the vehicular nodes. The issue of handling channel contention and broadcasting storm during the exchange of warning messages between the emergency vehicles and the Non Line Of Sight (NLOS) nodes is an area of major concern in congestive scenarios for vehicular network. NLOS nodes under emergency situation need to be effectively predicted such that the emergency vehicle is efficient enough in delivering the required information to the NLOS nodes in the stipulated time period with reduced overhead and time delay under a high degree of channel utilization. Weighted Distance Hyperbolic Prediction-Based Detection Scheme (WDHPBDS) is proposed as an effective mechanism that forecasts the NLOS nodes under dynamic moments of vehicular nodes using the merits of range free algorithm that integrates dynamic properties of hyperbolic properties. The simulation of WDHPBDS is investigated using EstiNet 8.1 network simulator by combining the data and management planes of the vehicular network. The results of WDHPBDS analyzed using warning message delivery rate, neighborhood awareness rate and channel utilization rate is confirmed to be 24%, 21% and 27% better to the compared NLOS detection schemes. [ABSTRACT FROM AUTHOR]

Rapid developments in Intelligent Transportation Systems (ITSs) have emerged as a new research field for building sustainable smart cities. VANET (vehicular ad hoc network) is one of the emergent transportation technologies that has a great impact on ensuring mainly traffic management and road safety in urban areas by effciently using data sharing among vehicles. To further increase the security and safety of passengers and drivers, ITSs are continually striving to make the fusion of emergent network technologies to provide more reliable and effcient services. Relating VANET to UAV (unmanned aerial vehicle) is an example of this fusion, where UAVs act as an assistant to vehicles aiming to extend the network connectivity while effciently avoiding obstacles (e.g., Buildings) and providing high data delivery ratios. However, VANET and UAV are still critical security subjects that must be addressed. Advanced Machine Learning (e.g., Deep Learning) techniques have recently been used to protect VANET and UAV communications against various cyber attacks that deteriorate the integrity, confidentiality, and availability of vehicular data. Thus, in this paper, we focus on reviewing related work on machine learning techniques for intrusion detection systems in VANET- and UAV-aided networks. We also highlight the main open research challenges in literature and provide hints for improving security in ITSs. [ABSTRACT FROM AUTHOR]

Geocast routing aims the transmission of a message to a community of vehicles in the same geographical area. The GPS information of vehicles, especially taxis, in a city gives reliable information about the traffic pattern of a city. With the help of this information smart models can be created to use when making routing decisions. In this paper, a smart geocasting protocol, GeoAKOM, is introduced and implemented by using the GPS data of taxis in Bursa city, Turkey. In GeoAKOM, the mobility pattern of city is analized in two levels as in GeoMobCon introduced earlier. In the macroscopic level analysis, the city is segmented into clusters using k-means clustering and optimal paths for a message is generated using these clusters. In the microscopic level, mobility based likelihood value is used to make routing decision. The message is transmitted if the encountered vehicle has a larger mobility based likelihood or contact history based likelihood value than the carrying vehicle as in GeoMobCon. Moreover, in GeoAKOM, the next cluster of a vehicle is predicted with the help of all k-order Markov model (AKOM) by using GPS records of all vehicles. Mobility based likelihood calculations are performed by using this prediction. With these features, GeoAKOM provides a robust solution for the vehicles which do not have large GPS record history and which are relatively new in the system. Simulations on real world taxi GPS data are compared for various number of clusters in terms of delivery ratio, average delay, and hop count by using the ONE simulator. Simulation results show that, GeoAKOM provides promising results when compared with GeoMobCon and the First Contact routing algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, 5G is playing a significant role in the efficiency of network security and creating more and faster channels for communication. 5G is evolving industries such as healthcare, education, marketing, transportation, and V2X (Vehicle-to-everything). In addition, 5G considers a new radio access technology that is adding new applications like the Internet of Things (IoT), Augmented Reality, Virtual Reality, connected cars, connected people-to-people, smart city, connected homes that are considered using higher bandwidth and low latency. Mainly, this paper is focusing on security challenges faced by the Vehicular ad-hoc network (VANET). VANET faces threats in three different fields; security, safety, and infotainment, which further have numerous attacks. More precisely, this research conducted an in-depth study and proposed a VANET trust model. Therefore the proposed model deals specifically with the "location closeness" parameter. Moreover, the trust model integrated with 5G cloud to support greater coverage, effective network density with respect to network infrastructure and IoT as well. Therefore, in this article, an effort has been put forward to implement the model using case studies to validate the trust model based on the "location closeness" parameter. The results proved the valid implementation of the model by identifying the trusted communication between the vehicles. [ABSTRACT FROM AUTHOR]

In recent years, urban population growth and the diversity of vehicles have increased. Location prediction in VANETs is extremely necessary for consumer applications such as routing, network management, knowledge dissemination protocols, and road cognition, among others. This could increase the performance of VANETs. In this paper a Kalman filter is used to predict the vehicle's future location. We conducted experiments exploitation each vehicle quality traces and model-driven traces. We quantitatively compare the prediction performance of a Kalman filter and neural network-based methods. This paper proposes a location prediction algorithm for nonlinear vehicular movement using an Extended Kalman filter (EKF). Evaluation of the ESCL-VNET algorithm with EKF assess the given better results. [ABSTRACT FROM AUTHOR]

In big cities, with heavy traffic, tracking vehicle movements undoubtedly generates big data, which can be cumbersome for real time analysis. In the other hand, the process of roadside unit (RSUs) deployment in Vehicular Ad-hoc Networks (VANETs) mainly relies on the analysis of vehicles trajectories, which is considered as big sequential data. The complexity comes from (i) the number of transactions recorded at each time and (ii) the redundancy of transactions in the database. In this respect, several research works focusing on RSUs deployment have been proposed. The thriving challenge would be to (i) reduce the deployment cost by minimizing as far as possible the number of used RSUs; and (ii) to maximize the coverage ratio. In this paper, we introduce a spatio-temporal RSU deployment method consisting of two main parts namely "finding representative transactions from the sequential database" and "computing coverage". The first one identifies the representative transactions, a.k.a transactions having a high utility, from a large sequential database. The second part computes a minimal set of junctions, where RSUs have to be ideally placed. The performed experiments show that our scheme outperforms its competitors in terms of cost, time performance as well as coverage ratio. [ABSTRACT FROM AUTHOR]