Software-Defined Approach for Communication in Autonomous Transportation Systems

Dhawankar, Piyush; Raza, Mohsin; Minh, Hoa Le; Aslam, Nauman

doi:10.4108/eai.14-7-2017.152924

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…The critical point here is to respect the latency requirements of vehicular communications due to the high cost of cellular networks for transferring control events. Therefore, methods for controlling and reducing latency of control plane are active topics in the literature regarding SDN implementations in V2X [83]- [85].…”

Section: ) Sdnmentioning

confidence: 99%

On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

2021

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5G technologies promise faster connections, lower latency, higher reliability, more capacity and wider coverage. We are looking to rely on these technologies to achieve Vehicle-to-Everything (V2X) communications, which increase the safety and autonomy of vehicles in addition to road safety, saving energy and costs. The integration of vehicular communication systems and 5G is the subject of many research. Nowadays, researchers address challenges such as automated and intelligent networks, cloud and edge data processing, network management, virtualization, security, privacy and finally interoperability. This paper provides a survey of the latest V2X use cases including requirements, and various 5G enabling technologies under consideration for vehicular communications. Subsequently, we first provide an interesting mapping between the three 5G pillars and V2X use case groups. Then, we present a summary of potential applications of enabling technologies for V2X use case groups. Finally, the open directions of research are discussed, and the challenges that await to be met are pointed out.

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Section: ) Sdnmentioning

confidence: 99%

On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

2021

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“…The SDN-based autonomous transportation system is presented in [23] in which V2X communications of the ADV network is presented. The RSU maintains local information about the surrounding environment and communicates with the ADV.…”

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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“…By leveraging software modules and virtualization of infrastructure and network functions, the SDVC architecture enables the centralized controller to flexibly and efficiently manage resource utilization. Compared with VC, the main advantages of SDVC are threefold: (1) reliable view of the mobility prediction and resource pool virtualization-improves location awareness service via a cloud-radio access network (C-RAN) and multi-access edge computing (MEC) [6,7]; (2) selection of dedicated mobile communication virtualization functions-increase bandwidth use and optimize routing protocols by leveraging fifth generation (5G) solutions (e.g., network slicing (NS)) and artificial neural networks (ANNs) [8][9][10]; (3) powerful VC network programming-allows the controller server to create and maintain a database containing wide information on the VC such as network topology [11,12]. In [13], SDVC employs SDN and cloud computing to enable the distribution of software updates on SDN-enabled vehicles.…”

Section: Introductionmentioning

confidence: 99%

Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey

Nkenyereye

Jang

2023

Electronics

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Vehicular cloud computing (VCC) and connected vehicles have prompted the intensive investigation of communication and computing solutions. As an important enabler, software-defined network (SDN) broadly changes the design of vehicle services, from resource allocation to ambitious autonomous cars. However, current VCC architectures face challenges that hinder the vision of providing reliable services to connected vehicles. As a result, deploying VC services using SDN network has emerged as a viable option. Therefore, software-defined VC architecture (SDVC) dynamically manages the control and resource utilization of VC by centralizing the overall knowledge. In addition, SDN stands as the representative technique of virtual resources and network function virtualization (NFV). NFV is integrated into SDVC frameworks to design extended SDVC (ESDVC) for dynamic, adaptive VC maintenance, VC network slicing management, and to meet constraint requirements such as network latency and reliable connectivity. This paper presents and discusses: (1) the architecture scenario of both SDVC and ESDVC; (2) the effective deployment methods enabling NFV and network slicing (NS) frameworks to customize VC frameworks; (3) challenges and future concepts of more VC services based on ESDVC architecture. From this survey, we believe readers would find relevant methods for realigning information dispersed across the SDVC, fifth generation (5G)-based VC, and NS domains and comprehending the relationships between these technologies while encouraging further debate on the fusion of 5G enabling technologies over SDVC to enable VC network slicing.

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Software-Defined Approach for Communication in Autonomous Transportation Systems

Cited by 5 publications

References 9 publications

On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

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

Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey

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