Automotive Networks Research Articles

2-Mbps Power-Line Communication Transmitter Based on Switched Capacitors for Automotive Networks

Nowadays, automotive wire harnesses have become very complex systems as the number of electronic components and Electronic Control Units (ECUs) inside a vehicle have increased dramatically. To manage this complexity, and to support the automotive systems of tomorrow, new communication techniques need to be investigated. In this work, a direct modulated Power-Line Communication (PLC) method is proposed to drastically reduce the number of interconnections and give advantages in cost, complexity, and weight. Two transmitter topologies based on a Switching Capacitor approach are presented and implemented in a Test Chip fabricated in a 180 nm HV-CMOS SOI technology. The proposal is validated by communication tests connecting the designed chip and a discrete component-based demonstrator receiver through an unshielded twisted pair cable. Compared to commercially available solutions, the proposed approach can reach a data rate of 2 Mbps, making it able to implement high-speed event-driven networks, such as the Controller Area Network (CAN).

Online Rerouting and Rescheduling of Time-Triggered Flows for Fault Tolerance in Time-Sensitive Networking

Time-sensitive networking (TSN) is an industry-standard networking protocol that is widely deployed in safety-critical industrial and automotive networks thanks to its quality-of-service (QoS) mechanisms, esp. deterministic transmission and bounded end-to-end delay for time-triggered (TT) flows. In this article, we focus on TT flows and address the issue of fault tolerance against permanent and transient faults with both spatial and temporal redundancy. We present an efficient heuristic algorithm for online incremental rerouting and rescheduling of disrupted flows, assuming the paths and schedules of existing flows stay fixed. It is complementary to and can be combined with offline routing and scheduling algorithms for achieving fault tolerance based on frame replication and elimination for reliability (FRER) (IEEE 802.1CB). Performance evaluation shows that our approach is able to better recover the system’s degree of redundancy (DoR) and has a higher acceptance rate than related work.

SRv6‐based Time‐Sensitive Networks (TSN) with low‐overhead rerouting

AbstractTime‐Sensitive Networks (TSN) aims at providing a solid underpinning for the support of application connectivity demands across a wide spectrum of use cases and operational environments, such as industrial automation and automotive networks. However, handling network updates in TSN entails additional challenges, stemming from the need to perform both flow rerouting and TSN schedule reconfiguration. To address this issue, we propose a software‐defined network (SDN)‐based approach for low‐overhead TSN network updates, exploiting segment routing over IPv6 (SRv6) for path control. To this end, we introduce the concept of TSN subgraphs in order to quickly reschedule the flows traversing the problematic area and propose a TSN‐aware routing heuristic to minimize the convergence time. We further describe the control plane implementation and its integration into Mininet, which empowers us to conduct a wide range of performance tests. Our evaluation results indicate that our approach yields faster recovery and reduces significantly the number of required reconfigurations upon failures, at the expense of a small SRv6 encoding/decoding overhead.

A new fuzzy-based method for energy-aware resource allocation in vehicular cloud computing using a nature-inspired algorithm

Vehicular cloud computing is a hopeful solution to utilize underused vehicle resources such as processing energy, storage space, Internet connection, etc. These resources can be shared among vehicles or rented by landlords for multiple purposes, such as meeting the hardware needs of automotive network services and applications. It is possible to meet the growing need for resources in the automotive network. Although this plan seems possible, its implementation has some problems. Several scholars have concentrated on architectural design to solve various difficulties and provide users with trustworthy service. This paper presents a fuzzy-based method to allocate resources in vehicular cloud computing using a nature-inspired (cuckoo search algorithm). The suggested algorithm is compared to some state-of-the-art algorithms. The outcomes illustrated that the recommended method outperforms other algorithms in terms of execution time, delay, and makespan.

Improving the Quality of Service (QoS) and Resource Allocation in Vehicular Platoon Using Meta-Heuristic Optimization Algorithm

The concept of vehicular platooning is emerging due to its self-driving capability, whereas a set of cars are arranged closely and that can drive safely even in high speed. Moreover the communication can also takes place between two platoons and hence the platoon leader can control the speed and direction. Traffic difficulties, like as collisions and delay at road junctions, need the use of platoon mechanisms. The most efficient vehicle traffic management in platoons is acknowledged in Intelligent Transportation System (ITS) for improving energy efficiency, road capacity, and road safety. Essentially, it is critical to analyse the connection characteristic of any two platoons in order to improve connectivity. As a result, under bi-directional platoon architecture, the possibility of connectivity between platoon members and Road Side Units (RSUs) is generated. Providing Quality of Service (QoS) to automotive network is difficult issue because elements such as asynchronous transmission or high mobility, crowded and fragmented channels, hardware defects, and various vehicular gadgets. As a result, it is extremely desired to utilize and distribute resources efficiently. Despite the existence of other bio-inspired meta-heuristic optimization methods, the Bacterial Foraging Algorithm has the benefit of simplicity and efficiency, and so may be used to a wide range of engineering applications. However, in the event of sophisticated analysis, the constraint is a lower convergence rate. To improve resource allocation, this study employs the Improved Bacterial Foraging Optimization based Channel Allocation (IBFOCA) technique. Set of channels allocated to platoon members are signified as bacteria’s position. By using latency, Signal-to-Interference Ratio (SINR) and Quality of Service (QoS), available channel is initially determined. This approach is used to find a channel that is acceptable for platoon members. Simulation results are used to examine the presentation of multi-channel distributions in platoons utilizing suggested optimization strategy. Signal-to-interference ratio is lowered by 29 decibel, throughput is raised by 69 kilobits per second, collision probability is reduced by 21.4 percent, and average batch size is 57 percent with a delay of 49 milliseconds.

Illegal Intrusion Detection for In-Vehicle CAN Bus Based on Immunology Principle

The controller area network (CAN) bus has become one of the most commonly used protocols in automotive networks. Some potential attackers inject malicious data packets into the CAN bus through external interfaces for implementing illegal operations (intrusion). Anomaly detection is a technique for network intrusion detection which can detect malicious data packs by comparing the normal data packets with incoming data packets obtained from the network traffic. The data of a normal network is in a symmetric and stable state, which will become asymmetric when compromised. Considering the in-vehicle network, the CAN bus is symmetrically similar to the immune system in terms of internal network structure and external invasion threats. In this work, we use an intrusion detection method based on the dendritic cell algorithm (DCA). However, existing studies suggest the use of optimization methods to improve the accuracy of classification algorithms, and the current optimization of the parameters of the detection method mostly relies on the manual tuning of the parameters, which is a large workload. In view of the above challenges, this paper proposes a new detection algorithm based on the particle swarm optimization algorithm (PSO) and gravitational search algorithm (GSA) to improve the dendritic cell algorithm (PSO-GSA-DCA). PSO-GSA-DCA achieves adaptive parameter tuning and improves detection accuracy by mixing optimization algorithms and using them to optimize the dendritic cell algorithm classifier. Additionally, DCA-based CAN message attribute matching rules (measured by information gain and standard deviation of CAN data) are proposed for matching the three input signals (PAMP, DS, SS) of the DCA. The experimental results show that our proposed scheme has a significant improvement in accuracy, which can reach 91.64%, and lower time loss compared with other correlation anomaly detection schemes. Our proposed method also enables adaptive tuning, which solves the problem that most models now rely on manual tuning.

Automotive Network Architecture Based on UAVs, SDN and Heterogeneous Edge Computing

Research subject is the use of UAVs, a multi-layer cloud MEC system and fog computing technology to reduce the load on the core network and reduce round-trip delay in vehicle networks and in the Smart City infrastructure; using multiple types of SDN controllers. Research method – analysis of standards and recommendations in the field of SDN, MEC technologies; Fog and building UAV networks, as well as vehicle networks. Main results. Three possible types of SDN controllers depending on the area of data exchange are described in detail; describes the sequence of actions for the distribution of UAVs in areas of increased load, as well as for choosing the level of processing; detailed theoretical information about VANET chips and MEC technology is presented. Practical significance. The presented architecture will reduce the load on the core network and ensure the provision of ultra-low transmission delays in vehicles and Smart City infrastructure.

Image Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle networking technologies related Ethernet Audio Video Bridge (AVB) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machin to Machine (RLMM) plays a significant role in reducing the latency between devices. The approach of RLMM on realistic test cases indicated that there was a latency reduction about 30.41% It is expected that the optimized settings for the actual automotive network environment can greatly shorten the time period in the development and design process. The results achieved from the experiments on the latency present in each function are trustworthy since average values are obtained via repeated tests for several months. It would considerably benefit the industry because analyzing the delay between each function in a short period of time is tremendously significant. In addition, through the proposed real-time camera and video streaming via optimized settings of AVB system, it is expected that AI (Artificial Intelligence) algorithms in autonomous driving will be of great help in understanding and analyzing images in real time.

GAN model using field fuzz mutation for in-vehicle CAN bus intrusion detection.

Controller area network (CAN) are widely used in smart vehicles to realize information interactions between electronic control units and other devices in vehicles. Owing to an increase in external communication interfaces, the cybersecurity of in-vehicle CAN bus networks is threatened. In-vehicle CAN intrusion detection systems with high detection rates and low false-negative rates have become important security protection measures for automotive networks. The boundary of the current machine learning-based in-vehicle CAN bus intrusion detection algorithm to determine the anomalous behavior triggered by CAN messages is unclear, and a validity check is required after the intrusion detection algorithm is designed. To solve the low coverage rate problem in the process of validating intrusion detection algorithms, an in-vehicle CAN fuzz-testing message generation model, the field-associative mutation generation adversarial network (FAMGAN), is proposed. To improve the defects of high randomness in generating messages in traditional fuzz-testing algorithms, FAMGAN adopts field division based on a conditional random field and the field association method based on the Apriori algorithm. Experiments were conducted on a real car using a code-built intrusion detection algorithm. The results demonstrate that FAMGAN can efficiently generate anomalous CAN messages and evaluate the performance of an in-vehicle CAN intrusion detection algorithm.

Improving Mobility-aware Routing in Vehicular Ad hoc Networks Considering Two Level Architecture

In VANET, moving vehicles each act as one moving node. Because of the large mobility of vehicles, the topology of the network is unstable. This instability of the network and its rapid changes have created a major challenge with regard to mobility management. Optimizing routing across automotive networks improves network node mobility management and minimizes clustering changes. As a result, in this study, the node clustering method was used for greater stability in node mobility. This study aims to significantly reduce changes in network topology, which means a decrease in clusters, as well as a significant increase in cluster survival. A random mobility model was considered in NS-3 and the speed of the nodes was randomized between 0 and 30 m/s. The simulation results showed that the proposed protocol has a high rate of accepted packages and the average survival of nodes and at the same time low cluster changes, which has a significant effect on improving mobility.

Asymmetric Symbol and Skew Sender Identification for Automotive Networks

Over recent years, many vulnerabilities have been exposed in vehicles, whose probability of occurrence will rise in the future due to connectivity and increasing system complexity. Even more serious that the Controller Area Network (CAN), widely used by Electronic Control Units (ECUs) to exchange safety-critical messages within a vehicle, does not provide any security measures. In this context, Intrusion Detection Systems (IDSs) have recently been proposed that use time characteristics in the analog transmission signal to identify the sending ECU and detect unauthorized messages. However, realistic application of proposed approaches is not yet tangible, with the full potential of time-based systems yet to be determined. In this elaboration, we therefore establish a foundation by working out robust time characteristics first and analyze root causes for their variation between ECUs. Combining the results with insights from related research, we deduce properties which help to design reliable and feasible IDSs for CAN. Finally, we emphasize these properties by presenting ASSASSIN, an IDS that uses time characteristics to identify the sender of a CAN message and assess authenticity. Achieving an average detection rate of 99.02 %, real-time capable classification and an adaptability to temperature fluctuations, we demonstrate the potential of time-based IDSs on a prototype setup and a real vehicle using resource-limited hardware, also contrasting them with well-elaborated voltage-based IDSs.

Analysis of Security Vulnerability Levels of In-Vehicle Network Topologies Applying Graph Representations

This article investigates cybersecurity issues related to in-vehicle communication networks. In-vehicle communication network security is evaluated based on the protection characteristics of the network components and the topology of the network. The automotive communication network topologies are represented as undirected weighted graphs, and their vulnerability is estimated based on the specific characteristics of the generated graph. Thirteen different vehicle models have been investigated to compare the vulnerability levels of the in-vehicle network using the Dijkstra's shortest route algorithm. An important advantage of the proposed method is that it is in accordance with the most relevant security evaluation models. On the other hand, the newly introduced approach considers the Secure-by-Design concept principles.

Research on Security Communication Technology of Internet of Vehicles Based on 5G Technology

With the improvement of the economic level and the development of infrastructure, the use of automobiles as a mode of transportation has become more and more common, and the number of cars has continued to grow. Based on this background, the rapid development of intelligent and networked automotive network communication technology, the birth of the Internet of Vehicles technology, has promoted the transformation of the automotive industry. This article gives the corresponding security requirements based on the security risks of IoV communication, briefly introduces the basic methods to realize the security of IoV communication, and proposes the service architecture, PKI architecture and multi-PKI system mutual trust mechanism to realize the security of IoV communication based on this.

Security Analysis of the TSN Backbone Architecture and Anomaly Detection System Design Based on IEEE 802.1Qci

With the development of intelligent and connected vehicles, onboard Ethernet will play an important role in the next generation of vehicle network architectures. It is well established that accurate timing and guaranteed data delivery are critical in the automotive environment. The time-sensitive network (TSN) protocol can precisely guarantee the time certainty of the key signals of automotive Ethernet. With the time-sensitive network based on automotive Ethernet being standardized by the TSN working group, the TSN has already entered the vision of the automotive network. However, the security mechanism of the TSN protocol is rarely discussed. First, the security of the TSN automotive Ethernet as a backbone E/E (electrical/electronic) architecture is analyzed in this paper through the Microsoft STRIDE threat model, and possible countermeasures for the security of automotive TSNs are listed, including the security protocol defined in the TSN, so that the TSN security protocol and the traditional protection technology can form a complete automotive Ethernet protection system. Then, the security mechanism per-stream filtering and policing (PSFP) defined in IEEE 802.1Qci is analyzed in detail, and an anomaly detection system based on PSFP is proposed in this paper. Finally, OMNeT++ is used to simulate a real TSN topology to evaluate the performance of the proposed anomaly detection system (ADS). As a result, the protection strategy based on 802.1Qci not only ensures the real-time performance of the TSN but can also isolate individuals with abnormal behavior and block DoS (denial of service) attacks, thus attaining the security protection of the TSN vehicle-based network.

Deep Learning to Predict the Feasibility of Priority-Based Ethernet Network Configurations

Machine learning has been recently applied in real-time systems to predict whether Ethernet network configurations are feasible in terms of meeting deadline constraints without executing conventional schedulability analysis. However, the existing prediction techniques require domain expertise to choose the relevant input features and do not perform consistently when topologies or traffic patterns differ significantly from the ones in the training data. To overcome these problems, we propose a Graph Neural Network (GNN) prediction model that synthesizes relevant features directly from the raw data. This deep learning model possesses the ability to exploit relations among flows, links, and queues in switched Ethernet networks and generalizes to unseen topologies and traffic patterns. We also explore the use of ensembles of GNNs and show that it enhances the robustness of the predictions. An evaluation on heterogeneous testing sets comprising realistic automotive networks shows that ensembles of 32 GNN models feature a prediction accuracy ranging from 79.3% to 90% for Ethernet networks using priorities as the Quality-of-Service mechanism. The use of ensemble models provides a speedup factor ranging from 77 to 1,715 compared to schedulability analysis, which allows a far more extensive design space exploration.

Potential of additive manufacturing for upstream automotive supply chains

ABSTRACT The COVID-19 pandemic impact on the global supply systems is continuously on the rise creating an increasing ripple effect across the automotive supply chain network. In this disruptive setting, Additive manufacturing (AM) is perceived by many supply chain professionals as well as by many global automakers amongst best options to handle the disruptions and boost the automotive supply chain resilience. This research is dedicated to analysis up to which extent Additive manufacturing (AM) is a miraculous remedy to automotive supply chain disruptions. To this end, we use the Indian automotive industry as case-model to identify major barriers towards AM wide deployment and derive recommendations on how AM can be successfully used to cope with automotive supply chain disruptions.

Safety Embedded System Integration in Automotive Network CAN Bus to Prevent Road Accidents

In recent years, safety driving has attracted growing interest from the scientific and the industrial communities. This interest is meant to reduce the road accidents caused by driver behavior, fatigue, drugs, etc. Drowsy driving is one of the first factors of road accidents; it compromises driving ability by reducing alertness and attentiveness, delaying reaction times, etc. In this paper, a solution to prevent road accidents based on driver drowsiness detection that can be connected to the other Electronic Control Units (ECUs) in the vehicle network through the Controller Area Network Protocol (CAN) Bus to act on the functionalities of the vehicle when the driver starts feeling (yawning) or become drowsy has been proposed. The proposed solution has achieved an average accuracy of 99.10%. The simulation results confirm that the proposed system can efficiently detect driver drowsiness and warn the driver before anything undesired happens in order to ensure a sufficient and acceptable level of safety. A comparison with recently published works in this field is also provided.

A machine learning approach for predicting hidden links in supply chain with graph neural networks

ABSTRACT Supply chain business interruption has been identified as a key risk factor in recent years, with high-impact disruptions due to disease outbreaks, logistic issues such as the recent Suez Canal blockage showing examples of how disruptions could propagate across complex emergent networks. Researchers have highlighted the importance of gaining visibility into procurement interdependencies between suppliers to develop more informed business contingency plans. However, extant methods such as supplier surveys rely on the willingness or ability of suppliers to share data and are not easily verifiable. In this article, we pose the supply chain visibility problem as a link prediction problem from the field of Machine Learning (ML) and propose the use of an automated method to detect potential links that are unknown to the buyer with Graph Neural Networks (GNN). Using a real automotive network as a test case, we show that our method performs better than existing algorithms. Additionally, we use Integrated Gradient to improve the explainability of our approach by highlighting input features that influence GNN’s decisions. We also discuss the advantages and limitations of using GNN for link prediction, outlining future research directions.

Robust authentication for automotive control networks through covert channels

Automotive control networks offer little resistance against security threats that come with the long-range connectivity in modern cars. Remote attacks that undermine the safety of vehicles have been shown to be practical. A range of security mechanisms have been proposed to harden resource-constrained embedded microcontrollers against malicious interference, including cryptographic protocols that establish the authenticity of in-vehicle message exchange. However, authenticated communication comes with repercussions on deployability and vehicle safety in terms of reliability, real-time compliance, backwards compatibility, and bandwidth and resource use.In this article we investigate benign, defencive uses of covert channels to implement and support vehicular message authentication mechanisms as a transparent, resource-conserving approach to automotive network security. We provide the first comprehensive evaluation of covert channels in Controller Area Networks (CAN) with respect to the attainable bandwidth and reliability of covert communication. Our analysis identifies timing-based covert channels as candidates to design a complementary nonce synchronisation channel that can enhance robustness against message loss in existing authentication schemes. We practically implement and evaluate this design on top of an open-source authenticated CAN communication library, showing that covert timing channels can improve communication robustness in benign circumstances, while not reducing the security guarantees of the underlying authentication primitives when under attack.

Assessments of Real-Time Communications over TSN Automotive Networks

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard.