ViFi

Guo, Katherine; Sanadhya, Shruti; Woo, T.Y.C.

doi:10.1145/2645892.2645893

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…Nakauchi et al [35] and Guo et al [36] presented similar proposals. Their focus was on an airtime RA method through the control of EDCA parameters, such as the Contention Window (CW) size and the Transmission Opportunities (TXOPs).…”

Section: Resource Allocation and Qos Supportmentioning

confidence: 76%

“…[41] STA virtualization [42] in testbed Several research efforts involved infrastructure sharing via network slicing [13]. Apart from experimentation isolation and analysis [38,42], most of the approaches consisted of airtime-based RA mechanisms for IEEE 802.11 network virtualization [14,35,36]. The focus of airtime scheduling has been extensively studied as a means to overcome the well-known IEEE 802.11 Performance Anomaly [43].…”

Section: Resource Allocation and Qos Supportmentioning

confidence: 99%

See 1 more Smart Citation

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Isolani

Kulenkamp

Márquez-Barja

et al. 2021

Sensors

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With the emergence of 5G networks and the stringent Quality of Service (QoS) requirements of Mission-Critical Applications (MCAs), co-existing networks are expected to deliver higher-speed connections, enhanced reliability, and lower latency. IEEE 802.11 networks, which co-exist with 5G, continue to be the access choice for indoor networks. However, traditional IEEE 802.11 networks lack sufficient reliability and they have non-deterministic latency. To dynamically control resources in IEEE 802.11 networks, in this paper we propose a delay-aware approach for Medium Access Control (MAC) management via airtime-based network slicing and traffic shaping, as well as user association while using Multi-Criteria Decision Analysis (MCDA). To fulfill the QoS requirements, we use Software-Defined Networking (SDN) for airtime-based network slicing and seamless handovers at the Software-Defined Radio Access Network (SD-RAN), while traffic shaping is done at the Stations (STAs). In addition to throughput, channel utilization, and signal strength, our approach monitors the queueing delay at the Access Points (APs) and uses it for centralized network management. We evaluate our approach in a testbed composed of APs controlled by SD-RAN and SDN controllers, with STAs under different workload combinations. Our results show that, in addition to load balancing flows across APs, our approach avoids the ping-pong effect while enhancing the QoS delivery at runtime. Under varying traffic demands, our approach maintains the queueing delay requirements of 5 ms for most of the experiment run, hence drawing closer to MCA requirements.

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Section: Resource Allocation and Qos Supportmentioning

confidence: 76%

Section: Resource Allocation and Qos Supportmentioning

confidence: 99%

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Isolani

Kulenkamp

Márquez-Barja

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…They did not user QoS satisfaction while devising their solution. Similarly, authors in [15] [16] have also targeted EDCA parameters to optimize and achieve better QoS however, their solution remain limited to 4 QoS access categories of WiFi and cannot be applied to current IoT networks with diverse range of QoS requirements.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Network Slicing and Resource Management for Diverse QoS in IoT Networks

Amur,

Zia,

Chiumento

et al. 2023

2023 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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The diversity of QoS requirements in modern IoT networks is expanding thus requiring more flexible and autonomous solutions. Network Slicing is an efficient technology that can enable efficient and flexible management of QoS in IoT networks by dividing network resources into small chinks and assigning them to the slices. However, proposed solutions in literature require predefined slices which require prior knowledge of network traffic thus limiting application of these solutions.To address this limitation, we have developed a dynamic and autonomous network slicing solution in a WiFi based IoT network using 5GEmpower SDN controller that creates slices depending on user traffic flows and priorities. Moreover, it autonomously estimates slice throughput requirements to subsequently allocate network resources to meet their QoS requirements. Our system is able to differentiate between multiple QoS requirements and provides framework for flexible prioritization policies for better QoS in IoT networks.

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Slicing Wi‐Fi links based on QoE video streaming fairness

Oliveira

Macedo

2021

Int J Network Mgmt

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Dynamic Adaptive Streaming over HyperText Transfer Protocol (DASH) video streaming is one of the dominant sources of traffic on the Internet, and this traffic is often delivered to users via Wi-Fi access points. This makes the quality of experience (QoE) of the service susceptible to degradation when the Wi-Fi link is underperforming, and it can also inflict QoE unfairness, creating situations where users connected to the same Wi-Fi network feel different levels of QoE. This article presents a system that improves the QoE fairness of DASH video transmission network slicing in the context of Wi-Fi networks.The proposed system slices the wireless resources unevenly among flows, taking into account the characteristics of the video and the QoE demands for each user. Experimental results show that fairness improves by 56% when compared to an unmanaged network, while the mean QoE is reduced by only 5%.

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ViFi

Cited by 12 publications

References 20 publications

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Autonomous Network Slicing and Resource Management for Diverse QoS in IoT Networks

Slicing Wi‐Fi links based on QoE video streaming fairness

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