Estimation of link interference in static multi-hop wireless networks

Padhye, Jitendra; Agarwal, Sharad; Padmanabhan, Venkata N.; Qiu, Lili; Rao, Ananth; Zill, Brian

doi:10.1145/1330107.1330143

Cited by 201 publications

(156 citation statements)

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“…The first category uses conflict graphs that capture interference conditions between link pairs. They build conflict graphs using either a simple distance-based criterion [19], [20] or exhaustive per-link measurements in an active [8], [13]- [17], [24], [49] or passive [18], [50]- [52] manner. These link-based conflict graphs are for indoor WiFi networks where transmission links are known.…”

Section: Conflict Graphs and Interference Modelsmentioning

confidence: 99%

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Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

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Abstract-Today, most spectrum allocation algorithms use conflict graphs to capture interference conditions. The use of conflict graphs, however, is often questioned by the wireless community for two reasons. First, building accurate conflict graphs requires significant overhead, and hence does not scale to outdoor networks. Second, conflict graphs cannot properly capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand how severe these problems are and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. Calibrated models are imperfect, and we study the impact of their errors on multiple steps in the process, from calibrating propagation models, predicting signal strengths, to building conflict graphs.At each step, we analyze the introduction, propagation, and final impact of errors by comparing each intermediate result to its ground-truth counterpart. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lower spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference.

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Section: Conflict Graphs and Interference Modelsmentioning

confidence: 99%

“…Existing proposals build artificial conflict graphs using a simple distance-based criterion [3], [19]- [21] or using signal strengths generated by simple RF propagation models with rule-of-thumb parameters [6], [22]. These simplifications, however, produce incorrect interference estimates that lead to poor performance [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

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“…In wireless networks, interference is better expressed in terms of probabilities because of the inherent fluctuation of the signal power due to fading effects and probabilistic dependency of error rates with signal to interference plus noise ratio (SINR). Prior measurement and modeling studies have elaborated on this aspect [13], [15]. Thus, in this work, we estimate via passive monitoring the nonbinary, pairwise interference between any two network nodes or links, in terms of probability of interference.…”

Section: Iiioverall Approachmentioning

confidence: 99%

Application Based Passive Measurement of Interference in Misbehaviour Detection

P.RODGE¹,

KUSHWAHA²

2014

IJAREEIE

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ABSTRACT:In this paper a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic has been proposed. This tool proposes the use of multiple sniffers being deployed across the network to capture wireless traffic trace thus does not requires any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Using machine learning approach these traces help to infer the carrier-sense relationship between network nodes. We are also able to detect selfish carrier-sense behavior.Experimental and simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. We use ns2 simulation to also validate the approach in a real Wireless LAN environment. KEYWORDS: 802.11 protocol, hidden Markov model, MAC layer misbehavior, interference I.INTRODUCTIONA Highly loaded network experiences a poor WiFi performance [1], [2]. In this work a technique to model and understand the wireless interference between network nodes and links in realistic WiFi network deployments is being presented. The goal is to achieve this without installing any monitoring software on the network nodes using a completely passive technique because any active measurement affects the network traffic. To achieve these goals, our approach uses a distributed set of "sniffers" that capture and record wireless frame traces. We then analyze the trace to understand the interference relations. This approach requires additional hardware for measurement, this can be viewed as a form of third-party solution. Such an approach is not new for example, DAIR [7], [8], Jigsaw [9], and Wit [10]. While these approaches provide many monitoring solutions, but do not provide any interference which is possible in the technique proposed.We are also able to detect the selfish behavior of the nodes . A selfish node can gain unfair share of the available bandwidth by manipulating different MAC protocol parameters, and can results in more collisions and can other transmitters to back off . We can detect the selfish carrier-sense behavior using the pairwise interference relationships discovered by the proposed technique which had been discussed only in one paper [11], that provides a limited solution using a nonpassive technique. Figure 1 Overview of the approach II.SYSTEM MODEL AND ASSUMPTIONS

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“…The collision domain of link is defined as the set of all links that contend with link . In general, identifying the interfering links is a hard problem, as links exhibit varying degree of interference [23]. Additional mechanisms requiring active or passive monitoring may have to be used [24].…”

Section: A Bottleneck Collision Domainmentioning

confidence: 99%