VCPS: Vehicular Cyber-physical Systems for Smart Road Services

Since traffic accidents occur frequently, a real-time video traffic reporting service is necessary in vehicular networks for a prompt response to accidents. Although the fifth-generation (5G) network provides a solution for real-time services in vehicular networks, the security and privacy of the services needs to be addressed first. The existing secure video reporting service schemes in 5G-enabled vehicular networks have significant computing, communication, and storage overheads, because of public key certificates, expensive bilinear pairing operations, and repeated video reporting to the cloud. To address these issues, we propose a secure edge computing-assisted video reporting service in 5G-enabled vehicular networks. In the proposed method, edge nodes complete the message verification and classification. Moreover, these nodes send the first received report message of the same accident to the designated official vehicles to realizes a local upload and download of video reports and to minimize the storage of repeated accident reports in the cloud. Security analysis indicates the proposed scheme is secure under the random oracle model and meets a series of vehicular networks requirements. In addition, performance evaluations show that the scheme achieves lower authentication overhead than existing signature schemes, and has lower total delay than other relevant video reporting service schemes.

Vehicular cyber physical systems (VCPS), by leveraging on advancements in sensing, wireless technologies and vehicular ad hoc networks (VANET) have improved driving experience and birthed systems like cooperative adaptive cruise control, and have formed the foundation for autonomous and platooned vehicles. However, its deployment is adversely affected by security concerns even as attackers continue to improve their attacks methods. In recent years, machine learning has become an invaluable tool for research, and investigations into its application in CPS security is increasingly active. Although, supervised learning techniques were used for most tasks initially, reinforcement learning due to the excellent results obtained in peculiar cases like the environment in which vehicles operate have become popular. Trust management systems are also very useful in identifying adversaries in a vehicular network. In this paper, a data-oriented trust-based method for improving the resiliency of vehicles to adversarial attacks is investigated using RL. Improving on other works that combine direct and indirect trusts and assume that vehicles interact for a long time, this method is suited for the dynamic environment vehicles operate in and the high mobility they experience. Specifically, the Q-learning learning algorithm is used to learn and adapt the weight used to estimate the trust value and therefore reflect the real environment. The simulation results obtained show that the proposed methodology is efficient and establishes the contributions of the application of RL to CPS security.

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the Internet. For the reliable data upload and download, TCP can be used for vehicular networks. However, TCP requires the connection initialization using three-way handshaking for the data exchange between two end systems over the Internet. Thus, the efficient operation of TCP is important for data services in the vehicular networks. This paper proposes a method of TCP Context Migration Scheme (TOMS) for the enhancement of data services in vehicular networks. TOMS provides vehicles with proactive TCP connection initialization using a moving TCP proxy as a cluster head, which will have the Internet connectivity with a Road-Side Unit (RSU). A cluster member can initiate its TCP connection toward its corresponding TCP end-system (e.g., server and peer) via the TCP proxy within its cluster. The TCP proxy performs the TCP connection set-up for the sake of other cluster member vehicles and acknowledges the received TCP segments toward these vehicles. When the TCP proxy moves out of the communication range of the RSU, it transfers the TCP contexts of other vehicles to another vehicle, which will play the role of a TCP proxy through the proposed TCP context migration scheme. Also, the RSU works as a fixed TCP proxy for handling the acknowledgement of TCP segments and TCP timer handling (e.g., persist timer and keepalive timer) when there happens the disconnection between the moving proxy and the RSU. Thus, it is shown that our TOMS outperforms the legacy TCP in vehicular networks.

Smart transportation in Vehicular Cyber Physical Systems (VCPS) are addressing a range of problems including reducing traffic accidents, congestion, fuel consumption, time spent on traffic jams, and to improve transportation safety. Smart transportation VCPS is expected to contribute an important role in the design and development of intelligent transportation systems. Intermittently Connected Vehicular Networks (ICVNs) are a kind of intelligent transportation systems, where due to mobility of vehicles there may be disconnections among the source and destination vehicles. A multi-hop scenario is used here, where a stationary road side unit exchanges data packets with a destination outside its communication range, using passing-by vehicles as information carriers. In order to deal with these ICVNs, an efficient routing scheme is necessary to withstand the maximum delay. We propose a routing scheme, which makes use of the movement history among the regions for routing a message. The proposed scheme ...

Management of vehicle traffic is a challenging task as it is non-deterministic by nature. Vehicular Cyber-Physical Systems (VCPS) is the emerging field of dynamics of vehicle management. Vehicle localization is considered an important task in VCPS. Many researchers proposed methodologies for this based on the Global Positioning System (GPS) which poses few location identification errors. Also, there are more vulnerabilities to the existing vehicular positioning system due to Zig-Zag attacks and bad-mouth attacks. In this work, an error-free and secure environment for communication between dynamically moving vehicle models has been proposed. In our proposed model a localization technique based on mathematical geometry which is capable of GPS outages and encompasses the dynamism of vehicle and on-road trajectory has been developed. The proposed model includes Extended Kalman filter-based routing to predict the neighbouring vehicle position. To avoid vulnerabilities created by the malicious nodes, a trust-based computation is performed by each node on its neighbours perceiving the authenticity of received messages. To validate the methodology, NS2 tool has been used to simulate the VCPS and to test the efficiency with different scenarios such as erroneous location, GPS outage, and malicious attack. The result shows that the proposed approach is more optimal and secure than the existing methodologies.

In Vehicular Cyber-Physical Systems (VCPS), the collected sensor data are always uncertain and conflicting. Dempster-Shafer (DS) evidence theory can effectively deal with uncertain information, but the Dempster's rule may produce counter-intuitive results when the information is conflicting. This paper proposed an improved approach for combining conflicting evidence with different weighting factors based on a novel dissimilarity measure. Firstly, a new dissimilarity measure mixing fuzzy nearness with the introduced correlation coefficient is proposed to characterize the divergence degree between two basic probability assignments (BPAs). Then, the weighting factors are developed by using the proposed dissimilarity measure. Finally, the Dempster's rule is chose to combine the revised sources. Simulation experiment shows that the improved method can effectively solve the problem of sensor data fusion in VCPS with better convergence performance.

Location tracking of mobile entity becomes a research challenge for applications in vehicular networks, mobile sensor networks and mobile adhoc networks. The GPS based tracking in vehicular networks has limitations in certain scenarios such as inside tunnels, tall buildings, dense forests, etc. Tracking of fast-paced mobile entity (say mobile node) finds applications in vehicular networks such as chasing, hit-and-run accidents, etc. Connected dominating set(CDS) provides a virtual backbone in adhoc network. This paper uses connected dominating set(CDS) based virtual backbone in vehicular adhoc networks for tracking of fast-paced mobile node's location. We show that using a simple heuristics that the location updates for mobile node can be reduced to the tune of 40%.

With the fulfillment of communication capabilities, vehicles are planned to have autonomous driving features with collaboration between vehicles, infrastructure units, pedestrians and networks elements. The main goal of vehicular communication is to provide accident-free automated driving while increasing the efficiency of road usage and supporting greener transportation. Because of the stringent requirements of vehicular networks such as low latency, high reliability, high data rate, 5G becomes emerging solution for autonomous vehicles. 5G vehicle-to-everything communication uses several communication models and technologies which make it open to several attacks such as eavesdropping, jamming, DoS, DDoS and sybil attacks. In this paper, a comprehensive literature survey about security and privacy solutions which can be applied to 5G vehicular networks are proposed. Solutions are categorized into 4 security services: authentication, confidentiality, availability and integrity. Challenges such as providing trust model for security services in vehicular networks and privacy concerns are proposed.

The architecture of C-RAN-based soft-defined vehicular networks can support increasing demands of various applications in the future vehicular cyber-physical systems, but lacks strong and practical security–enhancing mechanisms in the physical layer. In this article, we propose a hybrid beamforming and vehicle-selection framework for vehicle-to-infrastructure communications to broadcast high-speed confidential messages. A one-dimensional roadway scenario having multiple lanes is considered, and the concept of secure region and interference-selection region is, respectively, developed for each roadside unit. We formulate an optimization problem of maximizing secrecy sumrate with the constraint of orthogonality-based vehicle selection. The original problem is then divided into two subproblems subtly. In terms of the first subproblem, we, by exploiting the orthogonality assumption, obtain a corresponding secure beamformer for each vehicle located in the secure region to reduce the interference and confidential information leakage. Based on secure beamformers, the second subproblem is changed to be a vehicle-selection problem, which is then solved by adopting hierarchical agglomerative clustering method and implemented by two proposed novel algorithms blending vehicle selection with beamformer generation. Simulation results verify the effectiveness of the proposed algorithms in the respect of secrecy sumrate compared with the conventional zero-forcing beamforming using semi-orthogonal user selection algorithms. Furthermore, simulations show how our proposed algorithms resist against eavesdropping from collusive vehicles located in the same secure region by flexibly altering the size of secure region and interference-selection according to the traffic density.

The current data distribution method of vehicular network cannot satisfy the strict spatiotemporal constraints on the transmission of massive service data. Neither can the 5th generation mobile network (5G) meet the massive data demand of vehicular network services. To solve the problems, this paper designs an edge-assisted service data distribution method for vehicular network services. Specifically, the service data distribution was predicted by time series analysis through edge computing, based on the storage capacity of base stations. Then, a spatiotemporal constrained data sharing algorithm was proposed, which sets up a distribution tree to evaluate the importance of each vehicle in data transmission, and heuristically chooses the most important vehicles as seeds to speed up the vehicle-to-vehicle data sharing. Finally, the simulation experiment verifies that our method can greatly reduce the load of 5G network without breaking the spatiotemporal constraints of data transmission in vehicular network.

Vehicular Cloud, a new concept that merges Cloud Computing and Vehicular Network is an example of pervasive computing. This idea is based on of benefiting from the unused vehicles parked in a specific location for a period of time by running a computational operation. Because of the embedded access point in vehicles, Vehicular Clouds are main part of Internet of Things. One of the challenges in Vehicular Cloud is the communication between the vehicles. Because of dynamic nature of Vehicular Clouds, communication links may emerge and fail randomly which leads to connection failure in the entire network. Here we use the underlying network structure to propose a new strategy to mitigate this failure of service. We use label propagation method in order to cluster the network. In short, the idea is to connect a computation node with nodes within the community rather than nodes outside the community. This model increases the locality which is the main factor in performance optimization.

A survey on vehicular cloud computing

Integrated sensing and communication (ISAC) is a promising paradigm to provide both sensing and communication (S&C) services in vehicular networks. However, the power of echo signals reflected from vehicles may be too weak to be used for future precise positioning, due to the practically small radar cross section of vehicles with random reflection/scattering coefficient. To tackle this issue, we propose a novel mutual assistance scheme for intelligent surface-mounted vehicles, where S&C are innovatively designed to assist each other for achieving an efficient win-win integration, i.e., sensing-assisted phase shift design and communication-assisted high-precision sensing. Specifically, we first derive closed-form expressions of the expected echo power and achievable rate under uncertain angle information. Then, the communication rate is maximized while satisfying sensing requirements, which is proved to be a monotonic optimization problem on time allocation. Furthermore, we unveil the feasible condition of the problem and propose a polyblock-based optimal algorithm. Simulation results validate that the performance trade-off bound of S&C is significantly enlarged by exploiting mutual assistance.

In order to meet low-latency and ultrareliable requirements on safety services in vehicular networks, this paper proposes a novel Collision supervision and avoidance (CSA) algorithm for the contention based scalable media access control protocol. The twodimensional Markov chain model of adaptive backoff state transition criterion in CSA has been built, which could efficiently match the backoff states of nodes to the dynamic changes of vehicular networks. The scalable transmissions can be achieved through supervised trend and matching backoff mechanisms with three adaptive backoff modes. The packet transmit probabilities for the backoff modes have been derived with the theoretical result of the enhanced throughput. The simulation results show the remarkable scalability performance such as normalized throughput > 0.92, PDR > 86% and delay < 6.5ms even in the high-density and high-mobility environment.

Recent advancements in the automotive industry have led to the design of smart vehicles with high capacity resources for communication, sensing, processing, and storage of data. In the near future, it is envisaged that these resources will be harnessed and utilized to provide cloud services such as storage as a service, computation as a service, and sensing as a service. This paradigm of computing termed vehicular cloud, presents a lot of opportunities for the deployment of delay-sensitive applications in vehicular environment. However, high mobility of vehicles and rapidly changing topology of vehicular networks introduce new challenges such as instability of resources which make the management of resources in vehicular cloud complicated. Therefore, vehicular cloud computing require robust and mobility-aware resource management solutions to be designed. In order to achieve this goal, researchers and engineers need to be abreast with the techniques of resource management in vehicular cloud. This article presents a detailed survey of resource management tasks in vehicular clouds. A thorough introduction to vehicular cloud is presented initially. Then we identify and examine all the resource management tasks that are carried out in vehicular cloud by classifying them into three different phases: pre-resource assignment phase, resource-assignment phase, and post resource assignment phase. Proposed solutions to resource management challenges in literature at each phase are reviewed in detail. Unlike the existing surveys on vehicular clouds, this study covers all aspects of resource management such as resource brokering, resource demand prediction, resource allocation, and resource scheduling. Following the extensive survey, we also propose frameworks for resource monitoring and resource brokering for vehicular cloud. Finally, open issues and research directions for vehicular cloud resource management are presented.