A location-dependent runs-and-gaps model for predicting TCP performance over a UAV wireless channel

Kung, H. T.; Lin, Chit-Kwan; Lin, Tsung-Han; Stephen, Janahanlal; Vlah, Dario; Hague, Daniel; Muccio, Michael; Poland, Brendon; Suter, Bruce W.

doi:10.1109/milcom.2010.5680465

Cited by 10 publications

(10 citation statements)

References 15 publications

(11 reference statements)

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“…We found that the links are intermediate in their qualities for a significant amount of time, owing to the UAV moving in and out of range frequently; this might be a property specific to fixed-wing craft, but is important nonetheless as those make up the most efficient class of fliers. As a result, network layer protocols will need to be adapted to cope with this situation, or to work on top of link layer mechanisms which use, e.g., coding or retransmissions to mask the loss [7].…”

Section: Discussionmentioning

confidence: 99%

“…First, the periodic full-and intermediate-loss regions resulting from the circulatory flight pattern will be an important factor in UAV network protocol design, especially with use of legacy protocols such as Transmission Control Protocol (TCP) [6], [7] which are sensitive to packet loss patterns. Secondly, the significant presence of intermediate loss regions (16.1-18.6% in Table II) indicates that we could obtain substantial gains through the use of receiver diversity, provided that the losses at different receivers are generally not correlated.…”

Section: A Baseline Single-receiver Performancementioning

confidence: 99%

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Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

Kung

Lin

et al. 2010

2010 IEEE Globecom Workshops

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Abstract-We consider the problem of mitigating a highly varying wireless channel between a transmitting ground node and receivers on a small, low-altitude unmanned aerial vehicle (UAV) in a 802.11 wireless mesh network. One approach is to use multiple transmitter and receiver nodes that exploit the channel's spatial/temporal diversity and that cooperate to improve overall packet reception. We present a series of measurement results from a real-world testbed that characterize the resulting wireless channel. We show that the correlation between receiver nodes on the airplane is poor at small time scales so receiver diversity can be exploited. Our measurements suggest that using several receiver nodes simultaneously can boost packet delivery rates substantially. Lastly, we show that similar results apply to transmitter selection diversity as well.

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Section: Discussionmentioning

confidence: 99%

Section: A Baseline Single-receiver Performancementioning

confidence: 99%

Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

Kung

Lin

et al. 2010

2010 IEEE Globecom Workshops

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“…These two extremes are sometimes blended by locationbased statistical models [17,18]. However, we find that location information is generally a poor predictor of individual packet losses.…”

Section: Predictive Link-state Modelingmentioning

confidence: 81%

Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model

Tarsa

Comiter

Crouse

et al. 2015

Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing

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We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures caused by temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts due to dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques.We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle to enable easy integration across applications, devices, and networks.

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“…1 For a more detailed description of a similar campaign, see [4]. We graciously acknowledge those contributing researchers and engineers for their role in these experiments, including Chit-Kwan Lin, Dario Vlah, Dan Hague, Mike Muccio, Brendan Poland, Bob Gorman, and Jason Cassulis and the new channel observation:…”

Section: A Classification-based Gap Predictionmentioning

confidence: 99%

“…Statistical link models in the literature include applications of Markov chains to capture the first and second order statistics for 802.11 and GSM networks [3], [4], [5], and latent variable Gaussian processes to map location-based signal strength statistics [6]. However, these methods are not well suited to capturing predictive correlations that span many packet times; the number of Markov chain state transitions that must be trained explodes with the length of link state sequences, and physical drivers of packet loss like occlusions violate Gaussian assumptions.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Sparse Coding for Wireless Link Prediction in an Airborne Scenario

Tarsa

Kung

2013

MILCOM 2013 - 2013 IEEE Military Communications Conference

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Abstract-We build a data-driven hierarchical inference model to predict wireless link quality between a mobile unmanned aerial vehicle (UAV) and ground nodes. Clustering, sparse feature extraction, and non-linear pooling are combined to improve Support Vector Machine (SVM) classification when a limited training set does not comprehensively characterize data variations. Our approach first learns two layers of dictionaries by clustering packet reception data. These dictionaries are used to perform sparse feature extraction, which expresses link state vectors first in terms of a few prominent local patterns, or features, and then in terms of co-occurring features along the flight path. In order to tolerate artifacts like small positional shifts in field-collected data, we pool large magnitude features among overlapping shifted patches within windows. Together, these techniques transform raw link measurements into stable feature vectors that capture environmental effects driven by radio range limitations, antenna pattern variations, line-of-sight occlusions, etc. Link outage prediction is implemented by an SVM that assigns a common label to feature vectors immediately preceding gaps of successive packet losses; predictions are then fed to an adaptive link layer protocol that adjusts forward error correction rates, or queues packets during outages to prevent TCP timeout. In our harsh target environment, links are unstable and temporary outages common, so baseline TCP connections achieve only minimal throughput. However, connections under our predictive protocol temporarily hold packets that would otherwise be lost on unavailable links, and react quickly when the UAV link is restored, increasing overall channel utilization.

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A location-dependent runs-and-gaps model for predicting TCP performance over a UAV wireless channel

Cited by 10 publications

References 15 publications

Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model

Hierarchical Sparse Coding for Wireless Link Prediction in an Airborne Scenario

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