Practical Conflict Graphs in the Wild

Zhou, Xia; Zhang, Zengbin; Wang, Gang; Yu, Xiaoxiao; Zhao, Ben Y.; Zheng, Haitao

doi:10.1109/tnet.2014.2306416

Cited by 17 publications

(11 citation statements)

References 51 publications

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“…Moreover, most of these studies consider that nodes (or links), that are identified in conflict, are permanently in conflict, which is not true as shown in the previous section. This is for instance the case in [11,13,15].…”

Section: State-of-the-artmentioning

confidence: 91%

A Passive Method to Infer the Weighted Conflict Graph of an IEEE 802.11 Network

Abdewedoud

Busson

Lassous

et al. 2019

Ad-Hoc, Mobile, and Wireless Networks

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Wi-Fi networks often consist of several Access Points (APs) to form an Extended Service Set. These APs may interfere with each other as soon as they use the same channel or overlapping channels. A classical model to describe interference is the conflict graph. As the interference level varies in the network and in time, we consider a weighted conflict graph. In this paper, we propose a method to infer the weights of the conflict graph of a Wi-Fi network. Weights represent the proportion of activity from a neighbor detected by the Clear Channel Assessment mechanism. Our method relies on a theoretical model based on Markov networks applied to a decomposition of the original conflict graph. The input of our solution is the activity measured at each AP, measurements available in practice. The proposed method is validated through ns-3 simulations performed for different scenarios. Results show that our solution is able to accurately estimate the weights of the conflict graph.

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Section: State-of-the-artmentioning

confidence: 91%

A Passive Method to Infer the Weighted Conflict Graph of an IEEE 802.11 Network

Abdewedoud

Busson

Lassous

et al. 2019

Ad-Hoc, Mobile, and Wireless Networks

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, no previous works have dealt with large datasets’ analysis, corresponding to the conflict graphs of real-world Wi-Fi networks and its evolution over time. In [ 35 ], the authors addressed problems related to the construction of the conflict graphs using measurement-calibrated propagation models in order to avoid the need for detailed signal measurements (see later in Sect. 3 the detail about what Cisco does [ 36 ]).…”

Section: Related Workmentioning

confidence: 99%

Large-Scale 802.11 Wireless Networks Data Analysis Based on Graph Clustering

Capdehourat

Bermolen

Fiori

et al. 2021

Wireless Pers Commun

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This paper analyzes a large-scale dataset of real-world Wi-Fi operating networks, collected from more than 9,000 access points (APs) for 1 year. The APs are distributed among more than 1,200 educational centers in the context of a nation-wide one-to-one computing program, being most of them primary and secondary schools. The data corresponds to RSSI measurements between APs used to build the conflict graphs for each school Wi-Fi network. We propose a simple embedding for the Wi-Fi network conflict graphs based on classical graph features, which proves to be useful to analyze the behavior of the wireless networks, showing a high discrimination power among the different school networks. Moreover, we discuss some practical applications of the embedding. In particular, it enables to study the Wi-Fi network dynamics at each school, analyzing the conflict graphs temporal variations through clustering techniques. The presented methodology allows us to successfully separate the most stable scenarios from those with more significant variability, which therefore require more technical resources to optimize the network. Besides, we also compared the behaviour of the Wi-Fi networks of the different schools, which enable us to reuse the optimal configuration found for one school in all those sites that have similar conflict graph patterns.

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“…As nodes in a WMN can have several radio interfaces, the conflict graph takes into account the presence of several antennas/radios. The authors of Reference 8 also propose a new conflict graph that is able to represent interference between APs but also cumulative interference coming from several neighboring APs transmitting at the same time. In Reference 9, the authors assess interference between nodes through a set of received signal strength (RSS) measurements.…”

Section: State Of the Artmentioning

confidence: 99%

Inference of a clear channel assessment based conflict graph

Abdelwedoud

Busson

Guérin-Lassous

et al. 2020

Internet Technology Letters

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We consider an IEEE 802.11 network composed of several Access Points (APs) managed by one controller. The controller relies on pieces of information describing the network state as channels, load, associated stations, conflicts, etc. to configure and optimize the network. In this paper, we propose a method that infers the way the different channels are shared between APs according to the Clear Channel Assessment (CCA) mechanism. It is represented through a conflict graph where an edge exists if two APs are able to detect each other. As this detection is sometimes partial, the links are weighted. Our method relies on measures already available on most of Wi‐Fi products and does not generate any traffic except the transmission of these measures to the controller. A Markov network and an optimization problem are then proposed to infer the weights of the conflict graph. Our solution is shown accurate on a large set of simulations performed with the network simulator ns‐3.

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Practical Conflict Graphs in the Wild

Cited by 17 publications

References 51 publications

A Passive Method to Infer the Weighted Conflict Graph of an IEEE 802.11 Network

A Passive Method to Infer the Weighted Conflict Graph of an IEEE 802.11 Network

Large-Scale 802.11 Wireless Networks Data Analysis Based on Graph Clustering

Inference of a clear channel assessment based conflict graph

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