Diagnosing Wireless Packet Losses in 802.11: Separating Collision from Weak Signal

Rayanchu, Shravan; Mishra, Arunesh; Agrawal, Dheeraj; Saha, Saumitra; Banerjee, Suman

doi:10.1109/infocom.2008.124

Cited by 152 publications

(92 citation statements)

References 10 publications

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“…There have also been many previous approaches to diagnosing wireless networks. One approach is to deploy passive traffic monitors throughout the network to diagnose wireless pathologies [1,2,6,15,16] or to study wireless performance [14]. Kanuparthy et al [10] developed a tool to detect common wireless pathologies (such as low signal-to-noise ratio, congestion, and hidden terminals) by using both active probes and an additional passive monitor deployed within the network.…”

Section: Related Workmentioning

confidence: 99%

Home Network or Access Link? Locating Last-Mile Downstream Throughput Bottlenecks

Sundaresan

Feamster

Teixeira

2016

Passive and Active Measurement

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Abstract. As home networks see increasingly faster downstream throughput speeds, a natural question is whether users are benefiting from these faster speeds or simply facing performance bottlenecks in their own home networks. In this paper, we ask whether downstream throughput bottlenecks occur more frequently in their home networks or in their access ISPs. We identify lightweight metrics that can accurately identify whether a throughput bottleneck lies inside or outside a user's home network and develop a detection algorithm that locates these bottlenecks. We validate this algorithm in controlled settings and report on two deployments, one of which included 2,652 homes across the United States. We find that wireless bottlenecks are more common than accesslink bottlenecks-particularly for home networks with downstream throughput greater than 20 Mbps, where access-link bottlenecks are relatively rare.

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Section: Related Workmentioning

confidence: 99%

Home Network or Access Link? Locating Last-Mile Downstream Throughput Bottlenecks

Sundaresan

Feamster

Teixeira

2016

Passive and Active Measurement

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“…Many papers estimate channel condition based on historical loss information [17] [12]. softrate [21] uses BER of received packets to estimate bitrate that channel can support.…”

Section: A Rate Adaptation Based On 2d Modulation Schemesmentioning

confidence: 99%

“…The authors propose RRAA in [22] that leverages loss information in short frame window to estimate channel condition. COLLIE in [17] allows the receiver to send back error packets to the sender in order to diagonalize the cause of the error.…”

Section: A Rate Adaptation Based On 2d Modulation Schemesmentioning

confidence: 99%

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TiM: Fine-Grained Rate Adaptation in WLANs

Wang¹,

Zhang

et al. 2014

2014 IEEE 34th International Conference on Distributed Computing Systems

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Abstract-Channel condition varies frequently in wireless networks. To achieve good performance, devices need rate adaptation. In rate adaptation, choosing proper modulation schemes based on channel conditions is vital to the transmission performance. However, due to the natural character of discrete modulation types and continuous varied link conditions, we cannot make a one-to-one mapping from modulation schemes to channel conditions. This matching gap causes either over-select or underselect modulation schemes which limits throughput performance. To fill-in the gap, we propose TiM (Time-line Modulation), a novel 3-Dimensional modulation scheme by adding time dimension into current amplitude-phase domain schemes. With estimation of channel condition, TiM changes base-band data transmission time by artificially interpolating values between original data points without changing amplitude-phase domain modulation type. We implemented TiM on USRP2 and conducted comprehensive simulations. Results show that, compared with rate adaptation choosing from traditional modulation schemes, TiM can improve channel utilization up to 200%.

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“…Any frame S from a different sender overlapping with P is a potential cause of collision. If P does not overlap with any other frame, the packet loss is due to wireless channel errors rather than collisions [10], [32]. Because of the probabilistic nature of packet capture, sufficient statistics need to be built up to determine receiver-side interference.…”

Section: Learning Reciever Side Interferencementioning

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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Diagnosing Wireless Packet Losses in 802.11: Separating Collision from Weak Signal

Cited by 152 publications

References 10 publications

Home Network or Access Link? Locating Last-Mile Downstream Throughput Bottlenecks

Home Network or Access Link? Locating Last-Mile Downstream Throughput Bottlenecks

TiM: Fine-Grained Rate Adaptation in WLANs

Application Based Passive Measurement of Interference in Misbehaviour Detection

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