Wi-balance: Channel-aware user association in software-defined Wi-Fi networks

Coronado, Estefanía; Riggio, Roberto; Villalón, José; Garrido, A.

doi:10.1109/noms.2018.8406265

Cited by 21 publications

(35 citation statements)

References 28 publications

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“…Wi-Fi network [14]. In turn, Coronado et al also consider an SDN-enabled Wi-Fi network and present a user association scheme capable of detecting when trac is not distributed eciently and rescheduling user association transparently to APs and clients that are hampering network performance [16].…”

Section: Gómez Et Al Propose To Consider the Average Signal Strengthmentioning

confidence: 99%

“…In this context, machine learning is one of the most promising articial intelligence paradigm, designed to support intelligent radio terminals [40]. [12] Connected Objects Fingerprint Platform to analyze WiFi trace [13] Crowd Behavior Data Analysis [14] User Association WiFi SDN [15] Channel Allocation Metric Based Algorithm [16] Channel Allocation Wi-Fi SDN [17] Vehicles Location Survey [18] Trac Jam Prediction Combine data from dierent wireless sources [21] Optimal Channel Allocation [22] Heuristic Channel Allocation Dynamic channel selection [23] Channel Allocation Interference observed between APs [20] Channel Allocation Interference observed between clients and APs [24] Decrease Latency during hand-o…”

Section: Next-generation Networkmentioning

confidence: 99%

“…• OneLab [16] is a European initiative proposing to provide federated experimentation environments with dierent access technologies. OneLab brings together IoTLab-based experimentation environments, a wireless experimentation environment with Wi-Fi a/b/g/n nodes (NITOS), and the European PlanetLab Environment (PLE).…”

Section: Research Projectsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Medeiros

Neto

Lopez

et al. 2020

Preprint

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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

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Section: Gómez Et Al Propose To Consider the Average Signal Strengthmentioning

confidence: 99%

Section: Next-generation Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Medeiros

Neto

Lopez

et al. 2020

Preprint

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“…Coronado et al [Coronado et al 2018] apresentam o Wi-Balance, um algoritmo projetado para diminuir domínios de colisão entre os Pontos de Acesso (Access Points -APs) de uma rede e oferecer um esquema de associação, capaz de detectar situações nas quais o tráfego nãoé eficiente para os dispositivos dos usuários. O algoritmo parte de um AP aleatório da rede, analisa os canais utilizados por seus vizinhos e atribui o canal que estiver sendo menos utilizado.…”

Section: Trabalhos Relacionadosunclassified

“…O problema de coexistência pode ser mitigado entre dispositivos que empregam a mesma tecnologia de comunicação através do uso de um protocolo MAC, como CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) ou através do uso de uma técnica como o AFH (Adaptive Frequency Hopping), que permite que um dispositivo mude seu canal de transmissão quando detecta que o canal está sendo utilizado [Coronado et al 2018]. No entanto, tais soluções são impraticáveis quando os dispositivos usam diferentes tecnologias de comunicação, uma vez que essas técnicas são específicas para uma determinada tecnologia (por exemplo, AFH só encontra-se presente em dispositivos Bluetooth) [Chwalisz andWolisz 2018, Qu et al 2017].…”

Section: Introductionunclassified

Cross Technology Interference Minimization in Smart Environments

Ponte¹,

Galdino²,

Rocha³

et al. 2019

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP)

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The growth of the Internet of Things (IoT) paradigm, popularized the use of wireless devices in the most diverse environments, creating the concept of Smart Environments (SE). SEs can be anything from a factory floor to our homes and are equipped with intelligent devices that automate most human activities. These devices suffer from the coexistence problem, as they often use the 2.4 GHz ISM band to communicate. In this context, this article presents the CASE algorithm to minimize interference in those environments through the proper allocation of wireless channels. The results suggest that the proposed algorithm reduces total interference as well as maximizes packet delivery when compared to existing approaches.

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SD-RAN Interactive Management Using In-band Network Telemetry in IEEE 802.11 Networks

et al. 2022

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Future digital factories are becoming more and more softwarized. This introduces flexibility, but the industry also demands robustness and Quality of Service (QoS) support. While 5G envisions to enable real-time applications with strict performance requirements, IEEE 802.11 networks continue to be a viable option for indoor scenarios. However, IEEE 802.11 networks currently cannot be programmed fine-grained enough to fully support and ensure QoS. Besides, due to the unknown or coarse-grained monitoring information over the wireless links, detecting performance degradation is still challenging. In this paper, we propose a Software-Defined Radio Access Network (SD-RAN) interactive management approach assisted by In-band Network Telemetry (INT) in IEEE 802.11 networks. We design a system-level architecture that includes the network administrator in the management loop through monitoring, visualization, and configuration activities. To demonstrate the feasibility of our approach, we developed a prototype and evaluated its flexibility in a real-world testbed. We argue that, with the fine-grained network information from INT and the benefits of the Software-Defined Networking (SDN) paradigm, an SDN-tailored system can assist in troubleshooting and assess enhanced QoS delivery.

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Wi-balance: Channel-aware user association in software-defined Wi-Fi networks

Cited by 21 publications

References 28 publications

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Cross Technology Interference Minimization in Smart Environments

SD-RAN Interactive Management Using In-band Network Telemetry in IEEE 802.11 Networks

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