BENBI: Scalable and Dynamic Access Control on the Northbound Interface of SDN-Based VANET

Weng, Jiasi; Zhang, Yue; Luo, Weiqi; Lan, Wei-Ming

doi:10.1109/tvt.2018.2880238

Cited by 58 publications

(22 citation statements)

References 47 publications

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“…Different techniques have been used to design different routing protocols in VANET such as genetic algorithms [30], opportunistic algorithms [33], heuristic algorithms [7], probabilistic algorithms [19], game theory [21], network coding [10] [25] and network clustering [16] [9]. Furthermore, different protocols have been designed for network layer to attain different QoS services such as minimizing delay [8] [21] [23] [30], increasing link stability [15], minimizing communication cost [4], increasing delivery ratio [33], and security issues [24]. Regardless of techniques employed to infer the routing decision, the routing protocols are broadly classified into distributed protocols and centralized protocols.…”

Section: Related Workmentioning

confidence: 99%

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Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

Hawbani

Torbosh

Wang

et al. 2021

IEEE Trans. Fuzzy Syst.

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this paper modeled the multihop data-routing in Vehicular Ad-hoc Networks(VANET) as Multiple Criteria Decision Making (MCDM) in four steps. First, the criteria which have an impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by Fuzzy Membership Functions(FMF) which are interpolated based on the data collected from massive experimental simulations. Third, the Analytical Hierarchy Process(AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and, the TSK inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we designed a distributed and opportunistic data routing protocol, namely, VEFR (Vehicular Environment Fuzzy Router) which targets V2V (vehicle-to-vehicle) communication and runs in two main processes, Road Segment Selection(RSS) and Relay Vehicle Selection(RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results showed that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

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Section: Related Workmentioning

confidence: 99%

“…The probability that the packet has been received successfully is derived from the probability that the receiving power of the signal is greater than the threshold value , formulated in Eq. (24).…”

Section: Relay Vehicle Selection Attributes A) Transmission Distance mentioning

confidence: 99%

Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

Hawbani

Torbosh

Wang

et al. 2021

IEEE Trans. Fuzzy Syst.

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“…Besides, Reference [26] explains that SDN can be applied to safety surveillance and virtualization services. As the SDN controller requires to centralize global knowledge and configure the network, Reference [27] proposes a scalable and dynamic access control scheme to ensure the security of north bound interface. In order to support the cloud computing at the edge of network, Reference [28] proposes a networking architecture in which not only SDN technology is used but also fog computing is added to provide security services, traffic management, delay reduction and location aware functions.…”

Section: Sdn-based Vanetsmentioning

confidence: 99%

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Duo

Yoshinaga

et al. 2020

Sensors

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With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens.

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“…An SDN-based VANET, known as an SDVN, explores the potential flexibility and management capabilities achieved by deploying SDN-controllers in vehicles [3]. Data transmission in SDVN is applicable for multi-hop routing with the objective of minimizing end-to-end delays [4].…”

Section: Introductionmentioning

confidence: 99%

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

et al. 2020

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Content retrieval is becoming an emerging application on an integrated network structure referred to as Software Defined Network and Vehicular Ad hoc Network (SDVN). The SDVN is expected to flexibly provide efficient packet routing without deploying road side units. In such the infrastructure-less environment, however, it is challenging to retrieve contents from dynamic networks clustered with vehicles on roadways. As a solution to the problem, we propose a new network design by conjoining Named Data Network (NDN) with SDVN (NDN-SDVN). The new NDN-SDVN is designed as to improve stableness of the cluster formation and hence enables to efficiently perform the content retrieval over Vehicle-to-Vehicle (V2V) communications. Note that one of the most important problems in the V2V is how to select a cluster head. The proposed NDN-SDN adaptively determines the cluster head by using the SDN controller under the maximum Quality of Service (QoS) criterion, while reserving an assistance head based on a Moth Flame Optimization (MFO) algorithm to immediately recompose a new cluster with the back-off time. Moreover, the content retrieval in the NDN-SDVN can be performed over both intra-and inter-cluster communications. To this end, a Kalman filter with Neural Network (KF-NN) is applied for selecting a certain number of preferable vehicles in 1-hop in order to mitigate the broadcast storm problem that often occurs in an NDN. Content retrieval is initiated by sending interest packets including two additional fields of the timestamp and the target MAC address. The head vehicle of the SDN controller maintains ontology regarding the content information cashed by other vehicles, which significantly improves the packet delivery ratio. This NDN-SDVN design is developed on the OMNeT++ simulator and their advantageous results are evaluated in terms of the head lifetime, the cluster lifetime, the satisfactory rate, the latency and the packet delivery ratio.

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BENBI: Scalable and Dynamic Access Control on the Northbound Interface of SDN-Based VANET

Cited by 58 publications

References 47 publications

Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

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