An Optimization Framework for Data Collection in Software Defined Vehicular Networks

Wijesekara, Patikiri Arachchige Don Shehan Nilmantha; Sudheera, Kalupahana Liyanage Kushan; Sandamali, Gammana Guruge Nadeesha; Chong, J. Michael

doi:10.3390/s23031600

Cited by 11 publications

(24 citation statements)

References 53 publications

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“…Conventional mobile networks utilize network-grounded MM, while researchers have recently proposed and evaluated distributed MM with the aid of new platforms suchlike blockchain [10]. MM also involves quality of service grounded flow prioritization and allocating network resources for different services appropriately to satisfy minimum service requirements [11]. However, some networks can focus on maintaining continuous connectivity rather than adhering to different quality of service requirements in MM and it can depend on the type of mobile network considered [12].…”

Section: Article Historymentioning

confidence: 99%

A Review on Deploying Blockchain Technology for Network Mobility Management

Wijesekara

2024

International Transactions on Electrical Engineering and Comput

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In wireless mobile networks, Mobility Management (MM) is a critical aspect that involves different tasks such as handover management, location management, offloading, resource allocation, and others. Blockchain platform comprising of an array of linked blocks innately supports the incorruptibility, sustaining the authenticity, and sustain pseudonymous privacy. Traditional MM is centralized, in contrast to blockchain-grounded MM that is partially or fully decentralized in its approach. As the inaugural researchers to review on blockchain-grounded MM, we are proud to cluster the blockchain-grounded MM concept under 7 clusters and closely study each framework with respect to different aspects such as blockchain specific parameters, MM specific parameters, network parameters, and more. We gathered a sample of 92 source references by curating the articles for eligibility criteria examined from electronic archives utilizing a comprehensive and enduring way. Grounded on this inspection, blockchain-grounded MM reinforces the overall security, trust, and privacy of location management and resource allocation, blockchain-grounded secure handover, facilitating registration and authentication, automatic MM utilizing smart contracts, reducing vulnerabilities, repairing low quality data, coordination, fair scheduling, in offloading, and blockchain-grounded resource allocation. Close studying proves that out of blockchain grounded MM frameworks, 37.5% utilize blockchain grounded secure handover, 92.5% utilize simple blockchain architecture, 32.5% utilize PoW consensus, 100% distributed MM, 97.5% host grounded MM, 95% packet reordering tolerant MM, 52.5% QoS aware MM, and 12.5% proposed for 5G networks. Finally, we present the openings and obstructions of the concept of blockchain-grounded MM and then catering surmounting advices.

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Section: Article Historymentioning

confidence: 99%

A Review on Deploying Blockchain Technology for Network Mobility Management

Wijesekara

2024

International Transactions on Electrical Engineering and Comput

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“…Deep neural network To replace optimization models for routing [110] Achieve quasi-optimal performance Deep neural network 3D two space division, forwarding for FANETs [111] Better performance in packet delivery rate, energy-saving Deep neural network Hybrid stable delay and distance based routing [112,200] High packet delivery ratio, low latency and communication cost…”

Section: % Accurate Forecast In Reliability Predictionmentioning

confidence: 99%

“…However, this approach, which is usually used in the centralized architecture of KDN, is not the optimal way to collect data, as redundant data may be collected at the management plane, which yields higher communication costs, channel utilization, and latency. Therefore, a data collection optimization framework using Integer Quadratic Programming (IQP) has been proposed that minimizes total communication delay, communication cost, and communication overhead so that only a selected number of agents will unicast the collected data to the management plane while other nodes act as only broadcasting nodes [260]. Other techniques involve packet sampling techniques, which have been proposed to sample wildcard flow entries to be collected as data [261].…”

Section: Network Monitoring Frameworkmentioning

confidence: 99%

“…Similarly, a cellular base station in a mobile communication network manages call handoffs and communicates wirelessly using radio signals with mobile devices. Vehicles in a vehicular network communicate wirelessly, either using Dedicated Short Range Communication (DSRC) for Vehicle to Vehicle (V2V) communication or using mobile communication with the aid of a cellular base station for Vehicle to Infrastructure (V2I) communication [428]. Figure 10 depicts the KDN paradigm implementation in a heterogeneous network scenario consisting of a Wi-Fi network, a cellular network, a vehicular network, an optical network, and a wired network.…”

Section: Wireless Access Points Base-stations and Vehiclesmentioning

confidence: 99%

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A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

Self Cite

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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“…Traditional VANETs employ a conventional network architecture where each node serves as a router, combining forwarding and routing functions into a single unit [ 15 , 16 , 17 , 18 , 19 ]. Nevertheless, this conventional approach has notable drawbacks, such as its inability to accommodate heterogeneity, scalability issues, limited programmability, and other concerns [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

RC-LAHR: Road-Side-Unit-Assisted Cloud-Based Location-Aware Hybrid Routing for Software-Defined Vehicular Ad Hoc Networks

Kumar,

Raw

2024

Sensors

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The reliability of the communication link is quite common and challenging to handle as the topology changes frequently in vehicular ad hoc networks (VANETs). Another problem with VANETs is that the vehicles are from different manufacturers. Hence, the heterogeneity of hardware is obvious. These heterogeneity and reliability problems affect the message dissemination in VANETs. This paper aims to address these challenges by proposing a robust routing protocol capable of ensuring reliable, scalable, and heterogeneity-tolerant message dissemination in VANETs. We first introduced a hybrid hierarchical architecture based on software-defined networking (SDN) principles for VANETs, leveraging SDN’s inherent scalability and adaptability to heterogeneity. Further, a road-side unit (RSU)-assisted cloud-based location-aware hybrid routing for software-defined VANETs (SD-VANETs) that we call RC-LAHR was proposed. RC-LAHR was rigorously tested and analyzed for its performance in terms of packet delivery ratio (PDR) and end-to-end delay (EED), along with a comprehensive assessment of network traffic and load impacts on cloud infrastructure and RSUs. The routing protocol is compared with state-of-the-art protocols, Greedy Perimeter Stateless Routing (GPSR) and Opportunistic and Position-Based Routing (OPBR). The proposed routing protocol performs well as compared to GPSR and OPBR. The result shows that the EED is reduced to 20% and the PDR is increased to 30%. The network reliability is also increased up to 5% as compared to the OPBR and GPSR.

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An Optimization Framework for Data Collection in Software Defined Vehicular Networks

Cited by 11 publications

References 53 publications

A Review on Deploying Blockchain Technology for Network Mobility Management

A Review on Deploying Blockchain Technology for Network Mobility Management

A Comprehensive Survey on Knowledge-Defined Networking

RC-LAHR: Road-Side-Unit-Assisted Cloud-Based Location-Aware Hybrid Routing for Software-Defined Vehicular Ad Hoc Networks

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