Software-defined vehicular network (SDVN): A survey on architecture and routing

A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.

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“…Based on the degree of control of the SDN controller, there are three architectures for SDVNs [ 33 ].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

An Optimization Framework for Data Collection in Software Defined Vehicular Networks

Wijesekara

Sudheera

Sandamali

et al. 2023

Sensors

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“…Vehicular Ad-Hoc Networks (VANETs) are a perfect example of the distributed networks that can be considered a core of ITS. It allows various vehicular systems to exchange their information through different locations and patterns such as Vehicles-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) [3]. Although VANET offers a wide range of automotive services for paying passengers' needs and satisfying Quality of Service (QoS) constraints, it may not guarantee the local traffic databases and timely detection of road conditions; as well as lacks transmission reliability among vehicles that have adversely affected the rate of network lifetime and overall performance.…”

Section: Introductionmentioning

confidence: 99%

“…The energy-efficient routing protocol is designed by providing the most effective path for data gathering with taking into consideration energy conservation. According to [3][5] [6], the recent research in the routing methodology has been divided this topic into two main directions: (i) the topology-based routing methodology that concerns the nature of network topology, the packet forwarding in this type based on the information collected about the state of path/link (s) such as Ad hoc On-demand Distance Vector routing protocol (AODV), Source-Tree Adaptive Routing (STAR) protocol, Optimized Link-State Routing (OLSR) protocol; and (ii) the position-based routing methodology that concerns the geographic routing protocols, for instance, Fisheye State Routing (FSR) protocol, and the Temporally Ordered Routing Algorithm (TORA). In this method, the packet forwarding uses the information collected about the current physical position of the nodes.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Routing Protocol For Enhancing The Vehicular Ad-Hoc Network Communication

Ali

2022

Preprint

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Vehicular Ad-hoc Networks (VANET) have been significantly expanding to perform automotive services related to enhancing road networks, congestion flow, and even real-time traffic jams. Amid modern transportation systems, traffic quality has been relied on exchanging massive amounts of up-to-date information about their physical position and status with traffic management centers; this yields what’s called the ”data dissemination” problem. This type of phenomenon allows data propagation in a collaboratively and efficient manner to boost realtime performance. However, it brings several limitations in bandwidth usage and power consumption resulting in the premature end of network lifetime, as well as continuous break in path connection leading to lack of communication reliability and increasing packet loss probability. As a result, adjusting the rate of data dissemination over the network has become inescapable. The main objective of this study is to present a vehicle system design methodology that has the potential to achieve sufficient energy conservation as well as overcome real-time constraints. A dynamic real-time interaction architecture for vehicular networks based on an energy-efficient and QoS-aware routing technique and effective path estimation allowing for control of excessive dissemination of data traffic across the vehicular networks is proposed. The proposed architecture is also subjecting a new integration model between Software-Define Network (SDN) and fog computing for enhancing VANET performance. The simulation results show that the proposed architecture introduced an accurately controlling methodology in the transmission rate of data packets of VANET. There is a 60%-70% enhancement of the whole power consumption and network throughput, depending on the implementation of the proposed geographical routing methodology. The overall performance is enhanced in terms of packet delivery ratio, packet loss ratio, end-to-end delay time, and routing overhead.

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“…The operating environment or the integrated applications environment of these communication networks carries many challenges, such as intermittent connectivity, the provisioning of quality of service (QoS), and the heterogeneity of applications when a vehicle is in motion or immobile [ 6 ]. Nevertheless, we expect those types of communication networks to continue working, offering different multimedia services such as web navigation, streaming, social networks, and routine operations of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models

Torres-Alvarado

Morales-Rosales

Algredo‐Badillo

et al. 2022

Sensors

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The latest generation of communication networks, such as SDVN (Software-defined vehicular network) and VANETs (Vehicular ad-hoc networks), should evaluate their communication channels to adapt their behavior. The quality of the communication in data networks depends on the behavior of the transmission channel selected to send the information. Transmission channels can be affected by diverse problems ranging from physical phenomena (e.g., weather, cosmic rays) to interference or faults inherent to data spectra. In particular, if the channel has a good transmission quality, we might maximize the bandwidth use. Otherwise, although fault-tolerant schemes degrade the transmission speed by solving errors or failures should be included, these schemes spend more energy and are slower due to requesting lost packets (recovery). In this sense, one of the open problems in communications is how to design and implement an efficient and low-power-consumption mechanism capable of sensing the quality of the channel and automatically making the adjustments to select the channel over which transmit. In this work, we present a trade-off analysis based on hardware implementation to identify if a channel has a low or high quality, implementing four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines. We obtained the best trade-off with an accuracy of 95.01% and efficiency of 9.83 Mbps/LUT (LookUp Table) with a hardware implementation of a Decision Tree algorithm with a depth of five.

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Software-defined vehicular network (SDVN): A survey on architecture and routing

Cited by 43 publications

References 52 publications

An Optimization Framework for Data Collection in Software Defined Vehicular Networks

An Optimization Framework for Data Collection in Software Defined Vehicular Networks

Energy-Efficient Routing Protocol For Enhancing The Vehicular Ad-Hoc Network Communication

Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models

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