Vehicular Cyber-physical System Research Articles

6G‐based resource utilization framework in software defined hybrid vehicular networks

SummaryAdvanced development in technology raises a huge number of data sources that generate real time data for processing. To process such huge and complex real time data, there is a need for high processing units and more computing resources available at the nearest end, which causes high deployment cost and needs distributed data processing units at different locations. To concern this issue, we proposed a 6G‐based Resource Utilization Framework using SDN in the hybrid vehicular network (HVN). The key objective of this framework is to provide the computing vehicles as resources to complete the task efficiently. Three different technologies are combined into vehicular networks such as Vehicular Fog Computing, Vehicular Edge Computing, and Vehicular Cyber Physical System into integrated HVN. A layered architecture is used to define the data flow and control with the support of SDN and also the fast delivery of data by using 6G cellular communication in the proposed HVN. This scheme uses various algorithms to provide resources through the fast communication between vehicles in Hybrid Vehicular network. An extensive simulation is performed on the proposed scheme using the NS3 simulator. The simulation results outperform the performance of the proposed scheme in terms of various key computing parameters.

Joint Control of Power, Beamwidth, and Spacing for Platoon-Based Vehicular Cyber-Physical Systems

In a platoon-based vehicular cyber-physical system (VCPS), the traffic capacity and the fuel efficiency can be significantly improved by maintaining a small inter-vehicle distance (IVD). Meanwhile, the vehicle-to-infrastructure (V2I) communications provided by the roadside unit (RSU) are important for promoting the development of autonomous vehicles and reshaping the driving experience. However, as maintaining a small IVD inside the platoon may result in severe inter-beam interference, there is a tradeoff between the traffic capacity and the communication efficiency. In this paper, we formulate an optimization problem to minimize the IVD (a.k.a spacing) under the V2I quality-of-service (QoS) requirements by jointly controlling the transmit power, the beamwidth, and the IVD in a platoon-based VCPS. The traffic and channel uncertainties significantly complicate the analytical characterization of the impact of IVD, transmit power, and beamwidth on the QoS requirements. To solve the intractable challenging problem and capture the inherent timescale difference between the communication and vehicular systems, we propose a novel data-driven two-timescale reinforcement learning framework to control the beamforming and the spacing of the platoon at two timescales. With elaborately designed state, action, and reward, we develop a soft actor-critic based two-timescale deep reinforcement learning (DRL) algorithm to learn an effective policy for the control-communication co-design problem. Simulation results validate the effectiveness of spacing control and show that the proposed two-timescale DRL algorithm can significantly improve the traffic capacity and reduce the air drag coefficient while satisfying the QoS requirements of V2I communications.

The Detection Data Processing Mechanism for Vehicular Cyber Physical System in IoT Environment

The Detection Data Processing Mechanism for Vehicular Cyber Physical System in IoT Environment

Multimodal Named Data Discovery With Interest Broadcast Suppression for Vehicular CPS

Cyber-physical system (CPS) provides a well-organized integration between communication , computation , and control (3C) technologies. CPS has been widely used in the vehicular networks and it requires to discover multimodal data from the physical system to make appropriate decisions and actions, for example, congestion warnings, applying brakes, adjusting speed limits, etc. Information discovery and availability at individual network elements is one of the fundamental foundations of CPS. In this paper, we proposed two multimodal network information discovery schemes for vehicular CPS using the Named Data Networking (NDN). One of the proposed schemes simply modifies the pull-based NDN communication mechanism to discover multimodal multi-hop data from the network and the other scheme uses the Interest broadcast suppression (IBS) mechanism. The proposed Interest broadcast suppression scheme adapts the holding time technique to defer the Interest forwarding and its computation involves the hop-count, distance, and other network parameters. Simulation results show that the proposed schemes discover about 172 and 162 percent more multimodal information from approximately 283 and 210 percent more network area by suppressing approximately 50 percent of the Interest broadcast storm in highway and the urban traffic scenarios, respectively.

MC-Safe

In a Vehicular Cyber Physical System (VCPS), ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, then safety messages can have a much better chance to meet their real-time deadlines. In this article, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. During normal driving, MC-Safe monitors periodic beacons sent by other vehicles and estimates the communication delay on all the channels. Upon the detection of a potential accident, MC-Safe leverages a novel channel negotiation scheme that allows all the involved vehicles to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. MC-safe also features a novel coordinator selection algorithm that minimizes the delay of channel negotiation. Once a channel is selected, all the involved vehicles switch to the same selected channel for real-time communication with the least amount of interference. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 23.4% lower packet delay on average compared with other well-designed channel selection baselines.

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems

In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon’s moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon’s driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

Efficient Data Broadcast Mitigation in Multisource Named-Content Discovery for Vehicular CPS

Conventionally, a node or vehicle in the Named Data Networking (NDN) sends an interest packet to discover information and receives a single data packet in response. However, in the case of named data Vehicular Cyber-Physical System (VCPS), a vehicle may require content from multiple vehicles in the network. Therefore, NDN forwarding daemon must receive multiple data packets in response to a single interest. Moreover, the data broadcast storm worsens in the named data VCPS due to the broadcast nature of the wireless link. In this letter, we propose the data packet broadcast suppression method for any multisource content discovery in VCPS. The proposed scheme distributively selects the potential data forwarding vehicles based on the neighbor distance, node density, and the closeness to the data forwarding line of sight. Our simulation results show that the proposed scheme alleviates 55% data traffic, 17% average per-hop delay, and achieves 7% more data discovery ratio.

Cybersecurity Measures for Geocasting in Vehicular Cyber Physical System Environments

Geocasting in vehicular communication has witnessed significant attention due to the benefits of location oriented information dissemination in vehicular traffic environments. Various measures have been applied to enhance geocasting performance including dynamic relay area selection, junction nodes incorporation, caching integration, and geospatial distribution of nodes. However, the literature lacks toward geocasting under malicious relay vehicles leading to cybersecurity concern in vehicular traffic environments. In this context, this paper presents cybersecurity measures for geocasting in vehicular traffic environments focusing on security oriented vehicular connectivity. Specifically, a vehicular intrusion prevention technique is developed to measure the connectivity between the cache agent (CA) and cache user (CU) vehicles. The connectivity between static transport vehicles and CA/CU is measured via vehicular intrusion detection approach. The performance of the proposed vehicular cybersecurity measure is evaluated in realistic traffic environments. The comparative performance evaluation attests the benefits of security oriented geocasting in vehicular traffic environments.

CPS-Based Reliability Enhancement Mechanism for Vehicular Emergency Warning System

The cooperative driving system is a typical vehicular cyber-physical system, and it can integrate the vehicular cyber system with the vehicular physical system for the optimization of the cooperative driving at traffic intersections. The use of Vehicle-to-Everything (V2X) enables moving vehicles to communicate with each other, share Emergency Warning Messages (EWM) and provide drivers with pre-collision warnings in the vicinity of the intersection. Although Vehicle Ad Hoc Networks (VANET) can provide the access to vehicular cooperative driving systems, EWM transmission delay, error and redundancy often happen in the vicinity of traffic intersection. In order to improve the quality of the EWM propagation, this paper proposes an Optimal Transmission Reliability Enhancement Mechanism (OTREM) for the development of the cooperative driving systems. Our OTREM creates the strategy of the vehicular role change using the modified finite automata as the solution to the above EWM transmission problems. The experimental analyses show the proposed approach can significantly decline transmission delays, improve propagation accuracy, lessen EWM redundancy and raise propagation rates of EWM.

Tensor-Train Fuzzy Deep Computation Model for Citywide Traffic Flow Prediction

Accuracy is extensively considered a key issue for traffic big data prediction in a vehicular cyber-physical system (VCPS). Deep learning with super performance has been successfully applied to traffic prediction for feature learning. However, uncertain traffic big data pose a remarkable challenge on current deep learning models, which work in a vector space in a deterministic manner and fail to learn the features of uncertain traffic data. This study solves the problem of citywide traffic flow prediction to satisfy the accuracy requirement of the VCPS from the perspective of users. In this study, a tensor-train fuzzy deep convolution (TFDC) approach is first proposed to satisfy the accuracy requirements of traffic flow prediction. Moreover, the Tucker deep computation (T-TFDC) approach for TFDC is proposed to satisfy the prediction accuracy requirements with low computational complexity. The TFDC model is built upon the fuzzy deep convolutional network, which uses unified tensor data representation for spatio-temporal traffic flow data. The key idea of the T-TFDC is to introduce Tucker decomposition into the TFDC model to compress parameters for traffic flow feature learning. Furthermore, learning algorithms for training TFDC and T-TFDC model parameters are devised on the basis of the back-propagation strategy. Experimental results on the TaxiBJ and BikeNYC data sets verify the effectiveness and efficiency of the proposed approaches over state-of-the-art methods.

Fuzzy-Based Channel Selection for Location Oriented Services in Multichannel VCPS Environments

Location-oriented services in vehicular cyber-physical system (VCPS) have witnessed significant attention due to their potentiality to address traffic safety and efficiency related issues. The multichannel communication aids these services by tuning their overall performance in vehicular environments. Related literature on multichannel communication focuses on interference as channel quality measure. However, uncertain mobility and density of vehicles significantly affect channel quality apart from interference. The static quantification of channel quality is not suitable due to the dynamic characteristics of the channel quality parameters. In this context, this paper proposes fuzzy-based channel selection framework for location-oriented services in multichannel VCPS environments. A system model is presented for deriving channel access delay (CAD) using Markov chain model. The channel quality is estimated using CAD and signal-to-interference ratio (SIR). The fuzzy logic-based channel selection framework is developed considering fuzzification and defuzzification of CAD and SIR. The comparative performance evaluation attests the benefit of the framework as compared to the state-of-the-art techniques in VCPS.

SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems

The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite.

Sensor Data Processing for Vehicular Cyber Physical System with Context Awareness

Sensor Data Processing for Vehicular Cyber Physical System with Context Awareness

Cloud-Assisted Context-Aware Vehicular Cyber-Physical System for PHEVs in Smart Grid

Plug-in Hybrid Electric Vehicles (PHEVs) can be one of the cost-effective options of modern intelligent transportation systems in smart grid (SG) which can balance the demand and supply by temporarily storing the electrical energy in their batteries. In this paper, we propose a new context-aware layered architecture for demand side management using vehicular cyber-physical system (VCPS) with cloud support. We have used the concept of Bayesian coalition game and learning automata for an intelligent context-aware data collection and processing using a new payoff function for the players in the coalition game. In the proposed scheme, vehicles are assumed as the players which sense the SG environment during their mobility and collect information from it. The players in the game perform actions such as alert generation, and information dissemination. For each action, players receive a feedback from the environment according to which they update their action probability vector. The performance of the proposed scheme shows that there is a reduction in energy shortage by 30%, and information processing delay of 10%–15%. In addition, there is an increase of 15% in energy sold back to the grid using the proposed scheme. The results obtained demonstrate the effectiveness of the proposed scheme.

Coalition Game-Based Secure and Effective Clustering Communication in Vehicular Cyber-Physical System (VCPS).

In this paper, we address the low efficiency of cluster-based communication for the crossroad scenario in the Vehicular Cyber-Physical System (VCPS), which is due to the overload of the cluster head resulting from a large number of transmission bandwidth requirements. After formulating the issue as a coalition formation game, a coalition-based clustering strategy is proposed, which could converge into a Nash-stable partition to accomplish the clustering formation process. In the proposed strategy, the coalition utility is formulated by the relative velocity, relative position and the bandwidth availability ratio of vehicles among the cluster. Employing the coalition utility, the vehicles are denoted as the nodes that make the decision whether to switch to a new coalition or stay in the current coalition. Based on this, we can make full use of the bandwidth provided by cluster head under the requirement of clustering stability. Nevertheless, there exist selfish nodes during the clustering formation, so as to intend to benefit from networks. This behavior may degrade the communication quality and even destroy the cluster. Thus, we also present a reputation-based incentive and penalty mechanism to stop the selfish nodes from entering clusters. Numerical simulation results show that our strategy, CG-SECC, takes on a better performance for the tradeoff between the stability and efficiency of clustering communication. Besides, a case study demonstrates that the proposed incentive and penalty mechanism can play an important role in discovering and removing malicious nodes.

Improved Predictive Contention Window-Based Broadcast Optimization Strategy in Vehicular Cyber-Physical System

Vehicles in the vehicular cyber-physical system should avoid intense competition by adjusting contention windows of media access control (MAC) protocol. Accordingly, we propose an improved prediction contention window adjustment mechanism to optimise the broadcast performance. The set should be updated via a feedback mechanism, so as to solve the problem of changeable vehicular communication scenarios. After that, an improved Markov model is designed to analyse the backoff procedure and to obtain the window sizes related to the attribute set, for the purpose of creating the reference table by Bayesian personalised ranking algorithm. Besides, an estimation mechanism based on hidden Markov is provided to predict the following vehicle's state and to generate a mapping table that consists of the attribute set and size of the window. Comparing with traditional adjustment schemes, extensive simulations demonstrate that the proposed strategy could optimise the broadcast performance effectively in terms of reducing collision probability and transmission delay.

Improved predictive contention window-based broadcast optimisation strategy in vehicular cyber-physical system

Improved predictive contention window-based broadcast optimisation strategy in vehicular cyber-physical system

SPGS: a secure and privacy-preserving group setup framework for platoon-based vehicular cyber-physical systems

Recently, the platoon-based vehicular cyber-physical system VCPS has attracted significant attention because the platoon based driving pattern can bring many benefits. In the platoon-based VCPS, the platoon members may change quite dynamically because vehicles can join or leave the platoon at any time. Therefore, how to securely and efficiently set up and maintain a platoon is a challenge. To address this issue, in this paper, we propose a secure and privacy-preserving group setup framework, called SPGS, for platoon-based VCPS. The key components of SPGS are two group setup policies that can be developed based on two kinds of techniques: attribute-based encryption and contributory key agreement. Based on these group setup policies, we propose two authentication protocols, respectively. The first one can authenticate all vehicles in the platoon simultaneously. The second one can guarantee anonymous authentication with traceability. With SPGS, a temporary platoon can be securely set up and maintained, and platoon merging/splitting can also be flexibly supported. Finally, we carry out extensive analysis to show the security and efficiency of our proposed SPGS. Copyright © 2016 John Wiley & Sons, Ltd.

Energy-efficient data dissemination strategy for roadside infrastructure in VCPS

In vehicular cyber-physical system (VCPS), beaconing mechanism is commonly deployed for the solar-powered roadside infrastructure to detect available passing-by vehicles for data dissemination. The traditional beaconing mechanism would cause a higher energy consumption due to the periodical beaconing procedure. To tackle this problem, we propose a new data dissemination strategy for roadside infrastructures by adjusting the beaconing interval to reduce the energy consumption. We model the beaconing procedure as a Markov model by observing the periodical beaconing results and then using the modeling result to obtain the relationship between the beaconing interval and the expectation of the single-vehicle discovery time. Then, with the average data transmission rate requirement, we can calculate the maximum beaconing interval. We also introduce a satisfaction degree based on the Sigmoid function to combine the decreased energy consumption and the decreased data rate. The satisfaction degree function allows the roadside infrastructure to obtain the optimal beaconing interval based on the required quality of service. The analysis of the results show that careful tuning of key parameters leads to improved energy efficiency and increased data rate of the data dissemination strategy.

A Survey on Platoon-Based Vehicular Cyber-Physical Systems

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another vehicle and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared with driving individually, such a platoon-based driving pattern can significantly improve road capacity and energy efficiency. Moreover, with the emerging vehicular ad hoc network (VANET), the performance of a platoon in terms of road capacity, safety, energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of a VANET. Such a complex system can be considered a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on a platoon-based VCPS. We first review the related work of a platoon-based VCPS. We then introduce two elementary techniques involved in a platoon-based VCPS, i.e., the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in a platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control, platoon-based vehicular communications, etc., all of which are characterized by the tightly coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions.