Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges

Mahmood, Adnan; Zhang, Wei Emma; Sheng, Quan Z.

doi:10.3390/fi11030070

Cited by 75 publications

(41 citation statements)

References 38 publications

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“…Even though there is an ongoing debate as to the best way to provide connectivity for future vehicles [129]- [131], vehicles may be exposed more heterogeneous wireless networks that have different costs and latencies than smartphones while they are moving; a cellular network is typically the only option for a smartphone to maintain the connectivity while it is moving at a similar speed with a vehicle. In United States, some of ongoing V2I services [122] are provided by government via DSRC that allows DSRC-compatible vehicle to freely receive the public services.…”

Section: A Service Requirementsmentioning

confidence: 99%

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Wang

Liu

Kim

et al. 2020

IEEE Commun. Surv. Tutorials

110

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As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computationintensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.

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Section: A Service Requirementsmentioning

confidence: 99%

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Wang

Liu

Kim

et al. 2020

IEEE Commun. Surv. Tutorials

110

View full text Add to dashboard Cite

show abstract

“…The SDN and MEC are networking and computing concepts for next-generation vehicular networks [13]. SDN separates the control plane and data plane entities.…”

Section: 11p Mac-based Enhanced Distributed Channel Accessmentioning

confidence: 99%

“…Moreover, Multi-access Edge Computing is a concept that allows MEC servers to collocate with different network elements to support network edge such as cellular base stations (BSs) including macro-eNBs, small-eNBs, wi-fi access points, radio network controller sites, routers, switches and optical unit [14]. The distributed topology of the SDN controller at each multi-access edge computing alleviates the computation of the global view of the network [13]. Certainly, SDN based VANETs architectures manage the network latency [15], enhances routing protocols, efficient utilisation of network resources [16] and selection of best routes [17].…”

Section: 11p Mac-based Enhanced Distributed Channel Accessmentioning

confidence: 99%

“…In SDMEV, the V2X UE end-device is considered as a simple forwarding network device where the control plane responsible for determining the packets routing path is set apart. Eventually, side link (SL) over PC5 [13] (direct mode with LTE PC5 interface) establishes wireless direct communication among the V2X UE end-devices. In 3GPP Release 14 for V2X [24], SL direct communication would provide end-to-end latency of 5 ms or less between the vehicles and over 99 percent packet delivery [24].…”

Section: A Access Layer (Data Plane)mentioning

confidence: 99%

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Software Defined Network-Based Multi-Access Edge Framework for Vehicular Networks

Nkenyereye

Islam

et al. 2020

IEEE Access

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Vehicular networks aim to support cooperative warning applications that involve the dissemination of warning messages to reach vehicles in a target area. Due to the high mobility of vehicles, imperative technologies such as software-defined network (SDN) and edge computing (EC) have been proposed for the next-generation vehicular networks. The SDN separates the control plane from data plane entities and executes the control plane software on general purpose hardware. On the other hand, EC aims to reduce the network latency and packet loss rate by pushing the computations to the edge of the network. Nevertheless, the current solutions that integrate SDN and EC could not satisfy the latency requirements for data dissemination of vehicle-to-everything (V2X) services. To bridge the gap between the two technologies, the conventional EC is enhanced to multi-access edge computing (MEC) by collocating the edge computing servers with the radio access networks. In order to improve the latency for V2X services, we propose in this paper, an SDN-based multi-access edge computing framework for the vehicular networks (SDMEV). In the proposed solution, two main algorithms are implemented. First, a fuzzy logic-based algorithm is used to select the head vehicle for each evolved node B (eNB) collocated with roadside unit (RSU) for the purpose of grouping vehicles based on their communication interfaces. Afterward, an OpenFlow algorithm is deployed to update flow tables of forwarding devices at forwarding layers. In addition, a case study is presented and evaluated using the object-oriented modular discrete event network (OMNeT++) simulation framework which includes the INET framework-based SDN. Simulation results depict that the data dissemination based-SDN supported by multi-access edge computing over SDMEV can improve the latency requirements for V2X services. INDEX TERMS Data dissemination, software-defined vehicular network, eNB-type RSU, multi-access edge computing, vehicular ad hoc network, fuzzy clustering.

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“…The firmness of these requirements makes current wireless technologies unsuitable, and one possible research direction that is gaining adhesion is to consider a Software Defined Network (SDN) based hybrid (LTE (Long Term Evolution) based and DSRC (Dedicated Short Range Communication) , etc.) vehicular network as the access network infrastructure to support these emerging services [5][6][7]. Indeed, the "logical" centralized control based on a thorough visibility of the network, combined with the fine-grained and programmable selection and forwarding treatments of flows, inherent to SDN, can bring a noticeable boost to the emergence of these services.…”

Section: Introductionmentioning

confidence: 99%

Towards Dynamic Controller Placement in Software Defined Vehicular Networks

Toufga

Abdellatif

Assouane

et al. 2020

Sensors

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The emerging SDVN (Software Defined Vehicular Network) paradigm promises to bring flexibility and efficient resource utilization to vehicular networks, enabling the emergence of novel Intelligent Transportation Services. However, as it was initially designed with wired network in mind, applying the SDN paradigm to a vehicular context faces new challenges related to the peculiar characteristics of this network (high node mobility and node density, and the presence of wireless links). In this paper, we focus on one of the critical architectural elements of SDVN, namely, the SDN Controller Placement, and promote the use of dynamic placement methods that take into account the dynamicity of vehicular networks' topology. We also describe the different approaches towards a dynamic controller placement and also propose an ILP (Integer Linear Programming) based dynamic placement method that adaptively readjusts the number and placement of controllers according to road traffic fluctuations. The proposed method is evaluated using a realistic traffic trace from Luxembourg City. Simulation results show that our approach outperforms the static approach as proposed in the literature.Most existing research work have been elaborated on the opportunities that the SDVN concept could bring when applied to vehicular networks [10]. However, some challenges need to be addressed to reach these expected opportunities. Among these challenges, we mention firstly, the network topology discovery, which is a crucial service in the Software Defined Vehicular Netowrk (SDVN) system [11]. Its main goal is to build and maintain an up-to-date view of the underlying network. The density and high mobility of vehicles make the design of this service a challenging task. Secondly, the connectivity between the vehicles and their controller is partly wireless [12]. In fact, vehicles can go through areas without network coverage, this could make the controller temporarily unreachable, which may impact network performance. Thirdly, the mobility of vehicles can result in frequent changes of their attached SDN controller. This impacts the established sessions with the initial controller, as well as an increase of network overhead due to inter-controller vehicles information exchange. The SDN controller placement is a key architectural element that can help mitigating the above mentioned challenges. It consists of computing the number of SDN controllers to deploy and derive their placement in the network. This choice must be done appropriately given the critical role of an SDN controller in this architecture. First, wireless link connectivity and quality must be considered in order to minimize the connectivity losses between vehicles and their controllers. Second, the SDN Controller coverage should be defined carefully taking into account the density and mobility of vehicles. This helps avoiding SDN controllers' overload as well as improving the performance of vehicle-to-controller communications, and also, minimizing the number of changes in the set of deploy...

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Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges

Cited by 75 publications

References 38 publications

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Software Defined Network-Based Multi-Access Edge Framework for Vehicular Networks

Towards Dynamic Controller Placement in Software Defined Vehicular Networks

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