An SDN-based access point virtualization solution for multichannel IEEE 802.11 networks

Vieira, Juan Lucas; Passos, Diego

doi:10.1109/nof47743.2019.9015186

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…LVAPs can be deployed on demand and dynamically adjusted to adapt to changing network conditions. In [463], the authors show how LVAPs can be used for efficient client handover in a global scope, which has proven to improve the flexibility of management of wireless networks. Furthermore, mobility management involves channel scheduling.…”

Section: Network Managementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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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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Section: Network Managementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…These works can be categorized into (1) MS-based approaches [8,9] and (2) network-based approaches [10,11]. Even though virtual AP management has recently been introduced for mobility support in enterprise WLANs, virtual AP management aims to remove authentication and reassociation (i. e., not to improve the channel scanning procedure) during handover through virtual AP migration between APs [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Kim¹,

Kim²,

Choi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there are few mobile stations (MSs) for the handover. Therefore, this paper proposes a reinforcement learning-based handover scheme with neighbor beacon frame transmission (MAN-BTS) to properly consider the use of N-BTS. The optimization equation is defined to maximize the expected reward to find the optimal policy and is solved using Q-learning. Simulation results show that the proposed scheme outperforms the comparison schemes in terms of the expected reward.

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ASAP: IEEE 802.11ax-based seamless access point handover for moving vehicles

Lv,

He,

2024

Vehicular Communications

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An SDN-based access point virtualization solution for multichannel IEEE 802.11 networks

Cited by 6 publications

References 8 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

ASAP: IEEE 802.11ax-based seamless access point handover for moving vehicles

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