Vehicular software-defined networking and fog computing: Integration and design principles

Nobre, Jéferson Campos; Souza, Allan M. de; Rosário, Denis; Both, Cristiano Bonato; Villas, Leandro A.; Cerqueira, Eduardo; Braun, Torsten; Gerla, Mário

doi:10.1016/j.adhoc.2018.07.016

Cited by 71 publications

(26 citation statements)

References 33 publications

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“…The constraint of Equation (6) limits the number of wavelengths over each link for both primary and backup in both directions (i.e., from i to j and j to i together). The constraints of Equations (7) and (8) ensure the feasibility of the connections so that a link between two nodes can be used if and only if it exists in the physical topology. The constraints of Equations (9) and (10) limit the maximum distance between RRUs and BBUs to M H for primary and backup paths, respectively.…”

Section: Objective Functionmentioning

confidence: 99%

“…Centralized cloud-based Radio Access Networks (C-RANs) represent an effective solution to design high-capacity radio access in 4G and 5G networks and to support challenging use cases [5], such as the ones of vehicular networks. C-RAN introduces unprecedented flexibility by efficient application of Network Function Virtualization (NFV) [6] jointly with Software Defined Networking (SDN) [5,7,8]. SDN can, in fact, provide suitable control and management support to optimally locate virtualized network functionalities to intelligent nodes in the cost and power efficiency perspectives.…”

Section: Introductionmentioning

confidence: 99%

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Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

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The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

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Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

View full text Add to dashboard Cite

show abstract

“…Furthermore, offloading algorithms achieve better packet delivery time and packet loss. The authors of [47] proposed the conceptual principles that integrate the fog/cloud network to improve user experiences and reliable vehicular communications in order to support vehicular applications with restricting QoS/QoE requirements. Cooperative driving services are enabled by architecture framework based on SDN, NFV, and MEC to support V2X network slices.…”

Section: Sdn Controller Assistance For Vm Migration Over Sdvcmentioning

confidence: 99%

Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration

Nkenyereye

Tama

et al. 2020

Sensors

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Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software- Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SDN OpenFlow controller. From SDVN, new vehicular architectures are introduced, for instance SD Vehicular Cloud (SDVC). In SDVC, vehicles are SDN devices that host virtualization technology for enabling deployment of cloud-based vehicular applications. In addition, the migration of Virtual Machines (VM) over SDVC challenges the performance of cloud-based vehicular applications due the highly mobility of vehicles. However, the current literature that discusses VM migration in SDVC is very limited. In this paper, we first analyze the evolution of computation and networking technologies of SDVC with a focus on its architecture within the cloud-based vehicular environment. Then, we discuss the potential cloud-based vehicular applications assisted by the SDVC along with its ability to manage several VM migration scenarios. Lastly, we provide a detailed comparison of existing frameworks in SDVC that integrate the VM migration approach and different emulators or simulators network used to evaluate VM frameworks’ use cases.

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“…To understand the structure of this security, the pseudo code is also given as follows. The pseudo code uses the interpolation order [39] of two and only three nodes for communication.…”

Section: Fl Prevention Mechanismmentioning

confidence: 99%

Secure Intelligent Vehicular Network Using Fog Computing

Erskine

Elleithy

2019

Electronics

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VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.

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Vehicular software-defined networking and fog computing: Integration and design principles

Cited by 71 publications

References 33 publications

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration

Secure Intelligent Vehicular Network Using Fog Computing

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