A Performance Measurement and Analysis Method for Software-Defined Networking of IoV

Jiang, Dingde; Wang, Zhihao; Huo, Liuwei; Xie, Shaowei

doi:10.1109/tits.2020.3029076

Cited by 28 publications

(8 citation statements)

References 24 publications

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“…Network traffic is a basic performance index to reflect the network status, and it is also the most basic parameter to measure the operational status of each network device. The complexity of the network has brought many new challenges such as low network performance and difficult network management 2,3 . The network traffic of network equipment in a period of time is an important parameter of network management.…”

Section: Introductionmentioning

confidence: 99%

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Lian

Tian

2021

Int J Communication

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Summary In order to improve the prediction accuracy of network traffic, a novel prediction model based on ensemble empirical mode decomposition and multiple models is proposed. In this study, ensemble empirical mode decomposition algorithm is introduced to decompose the network traffic and get several components. Approximate entropy is introduced to judge the complexity of each component. According to the results of approximate entropy, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is introduced to predict low complexity components. The advantages of each prediction model are used to predict the appropriate component. The final prediction results are obtained by adding the predicted values of each component. In order to solve the problem that the prediction performance of support vector machine and echo state network is affected by their parameters, an improved whale optimization algorithm is proposed to optimize the parameters of the model. Meanwhile, the calculation results of approximate entropy show that compared with the original network traffic, the complexity of each component obtained by ensemble empirical mode decomposition is reduced, which reduces the complexity of modeling. Three network traffic datasets with sampling periods of 10 ms, 1 s, and 10 min are collected. Compared with the other five state‐of‐the‐art prediction models, the case study results show that the proposed prediction model has better prediction accuracy and excellent statistical performance indicators.

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Section: Introductionmentioning

confidence: 99%

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Lian

Tian

2021

Int J Communication

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“…In future work, a more complex scenario where nodes have varying communication radius, noise strength, and signal attenuation during the localization process needs to be analyzed and studied. Based on this work, we were able to extend our algorithm to solve the problem of node localization for more complex scenarios, such as the Internet of vehicles 51 and localization in Industry 4.0. [52][53][54][55]…”

Section: Discussionmentioning

confidence: 99%

A weighted range‐free localization algorithm for irregular multihop networks

Yan

Zhou

Huang

et al. 2022

Int J Communication

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Summary Range‐free localization is a widely concerned technique that supports many essential network applications like coverage and clustering. Most existing localization technologies assume that nodes are uniformly distributed in a regular network with no obstacles and that propagation paths between pairwise nodes are close to straight lines. However, in real‐world scenarios, network topology is usually irregular, resulting in estimated distances that differ significantly from the corresponding physical distances and poor anchor deployment. These will lead to erroneous distance measurement and inaccurate localization results. This paper proposes a new algorithm, called the weighted range‐free localization algorithm (WRL for short), to improve the localization performance in irregular networks. Specifically, we first present a feasible scheme that can fulfill the information exchange and relative distance estimation for each node simultaneously. We then employ an optimal weight function to suppress the effect of cumulative errors and introduce the weighted bounding‐box algorithm to mitigate outlier location estimation induced by probably collinear anchor nodes. Simulation results show that, compared to state‐of‐the‐art methods, WRL improves localization accuracy while incurring lower computation and communication overheads in various irregular network topologies.

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“…For ITS applications involving IoV, the validation of the IoV network measurement method is important because it characterizes its measurement performance for different controllers established in the control plane layer. Jiang et al in [64] proposed IoV-based SDN. This architecture provided reliable systematic measurement analysis obtained to dynamically update the network topology using massive traffic flows.…”

Section: Iov Network Measurementmentioning

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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A Performance Measurement and Analysis Method for Software-Defined Networking of IoV

Cited by 28 publications

References 24 publications

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

A weighted range‐free localization algorithm for irregular multihop networks

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

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