Software-Defined and Fog-Computing-Based Next Generation Vehicular Networks

Zhang, Yaomin; Zhang, Haijun; Long, Keping; Zheng, Qiang; Xie, Xiaoming

doi:10.1109/mcom.2018.1701320

Cited by 46 publications

(28 citation statements)

References 14 publications

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“…The integrated architecture of SDN and Fog Computing (FC) for VANETs, named SDFC-VeNET, is proposed in [27]. The control layer and forwarding layer reside on edge consisting of multiple controllers in the control layer.…”

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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“…CPU use/network expansion CPU can be optimized and network expansion can be realized through, e.g., different types of routing assisted by SDN controllers [38]. [42], optimize operating expense [43] and radio resource [44], and increase security [45].…”

Section: Scalability and Mobility Of Iotmentioning

confidence: 99%

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Ding

et al. 2020

Sensors

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The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

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“…In most papers, when authors talk about "the fog computing architecture", they mean something like the structure shown in Fig. 1; examples include [7,11,25,55,61,63,71,72]. There are some small variations: e.g., some authors also allow communication among end devices [63] or among fog nodes [11], Sharma et al differentiate between "edge nodes" and "fog nodes" [55], while Zhou et al [72] extend the three layers of Fig.…”

Section: Architecture In Fog Computingmentioning

confidence: 99%

Notions of architecture in fog computing

Mann

2020

Computing

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Fog computing is becoming a popular paradigm for bringing the advantages of the cloud nearer to the network edge. This way, computational tasks can be offloaded from end devices to nearby fog nodes, thus benefiting from high computational power and low latency at the same time. Architecture plays a central role in fog computing. Many papers on fog computing address architectural questions. However, a closer look reveals that different papers use the term “architecture” for very different concepts. This is rooted in the multi-disciplinary nature of the fog computing paradigm. The different communities involved in fog computing—network, hardware, system software, application software—all use the term “architecture,” but with different meaning. To facilitate the mutual understanding of architectural issues in fog computing, this paper introduces a conceptual framework for reasoning about architecture in fog computing. This conceptual framework uses three independent dimensions to describe architecture. Based on the three architecture dimensions, several architecture views can be defined to serve the different viewpoints of the involved disciplines, and to highlight different aspects of the architecture. The conceptual framework is validated using a literature mapping study.

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Software-Defined and Fog-Computing-Based Next Generation Vehicular Networks

Cited by 46 publications

References 14 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

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Notions of architecture in fog computing

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