Robust Safety for Autonomous Vehicles through Reconfigurable Networking

Halba, Khalid; Mahmoudi, Charif; Griffor, Edward

doi:10.4204/eptcs.269.5

Cited by 15 publications

(10 citation statements)

References 22 publications

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“…A software-defined in-vehicle networking (SDIVN) architecture is proposed for the autonomous vehicle resiliency, robustness, and timely message delivery in [24]. In [25], authors present a resource allocation mechanism ADVs by combing mobile edge access computing (MEC) and SDN in autonomous AVNETs.…”

Section: Background and Related Workmentioning

confidence: 99%

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

et al. 2020

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The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as the network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G network can provide an excellent platform for autonomous vehicles because SDN offers open programmability and flexibility for new services incorporation. This separation of control and data planes enables centralized and efficient management of resources in a very optimized and secure manner by having a global overview of the whole network. The SDN also provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. In addition, it promises the overall improved performance. In this paper, we propose a flow-based policy framework on the basis of two tiers virtualization for vehicular networks using SDNs. The vehicle to vehicle (V2V) communication is quite possible with wireless virtualization where different radio resources are allocated to V2V communications based on the flow classification, i.e., safety-related flow or non-safety flows, and the controller is responsible for managing the overall vehicular environment and V2X communications. The motivation behind this study is to implement a machine learning-enabled architecture to cater the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently. This paper also presents a proof of concept for leveraging machine learning-enabled resource classification and management through experimental evaluation of special-purpose testbed established in custom mininet setup. Furthermore, the results have been evaluated using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). While concluding the paper, it is shown that the LSTM has outperformed the rest of classification techniques with promising results.

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Section: Background and Related Workmentioning

confidence: 99%

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

et al. 2020

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“…Shin et al [11] review network security enhancements by SDN, for instance using central dynamic access control, network separation and firewalls. Halba et al [3] show how SDN can improve safety and robustness of IVNs. In previous work, we examined the combination of SDN and TSN for in car networks [12].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The OpenFlow forwarding pipeline identifies network flows based on header information from layer 2 to layer 4. In recent years, use cases for SDN extended to the vehicular domain [3]. The SDN matching pipeline promises to be a powerful tool that can precisely identify and separate control flows in the network.…”

mentioning

confidence: 99%

Strategies for Integrating Controls Flows in Software-Defined In-Vehicle Networks and Their Impact on Network Security

Häckel,

Schmidt,

Meyer

et al. 2020

Preprint

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Current In-Vehicle Networks (IVNs) connect Electronic Control Units (ECUs) via domain busses. A gateway forwards messages between these domains. Automotive Ethernet emerges as a flat, high-speed backbone technology for IVNs that carries the various control flows within Ethernet frames. Recently, Software-Defined Networking (SDN) has been identified as a useful building block of the vehicular domain, as it allows the differentiation of packets based on all header fields and thus can isolate unrelated control flows.In this work, we systematically explore the different strategies for integrating automotive control flows in switched Ethernetworks and analyze their security impact for a softwaredefined IVN. We discuss how control flow identifiers can be embedded on different layers resulting in a range of solutions from fully exposed embedding to deep encapsulation. We evaluate these strategies in a realistic IVN based on the communication matrix of a production grade vehicle, which we map into a modern Ethernet topology. We find that visibility of automotive control flows within packet headers is essential for the network infrastructure to enable isolation and access control. With an exposed embedding, the SDN backbone can establish and survey trust zones within the IVN and largely reduce the attack surface of connected cars. An exposed embedding strategy also minimizes communication expenses.

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“…Halba et al use SDN for data interoperability and robustness in the in-vehicular network and thus, substantiate the benefit of SDN for in-car networks. They show SDN controller applications implementing robustness and safety with fast failover mechanisms [14]. However, they do not consider realtime requirements in their network design.…”

Section: Background and Related Workmentioning

confidence: 99%

Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication

Häckel

Meyer

Korf

et al. 2019

2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring)

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Future in-vehicular networks will be based on Ethernet. The IEEE Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time requirements in future car communication. Software-Defined Networking (SDN) extends the Ethernet control plane with a programming option that can add much value to the resilience, security, and adaptivity of the automotive environment. In this work, we derive a first concept for combining Software-Defined Networking with Time-Sensitive Networking along with an initial evaluation. Our measurements are performed via a simulation that investigates whether an SDN architecture is suitable for time-critical applications in the car. Our findings indicate that the control overhead of SDN can be added without a delay penalty for the TSN traffic when protocols are mapped properly.

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Robust Safety for Autonomous Vehicles through Reconfigurable Networking

Cited by 15 publications

References 22 publications

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

Strategies for Integrating Controls Flows in Software-Defined In-Vehicle Networks and Their Impact on Network Security

Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication

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