Link quality prediction in mesh networks

Farkas, Károly; Hossmann, Theus; Legendre, Franck; Plattner, Bernhard; Das, Sajal K.

doi:10.1016/j.comcom.2008.01.047

Cited by 75 publications

(39 citation statements)

References 15 publications

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“…On the other hand, nodes are capable of taking signal strength measures from other nodes on their own. This parameter is more valuable than determining actual location of the other nodes, as it more directly represents the potential for successful communication, as noted in [6]. This paper proposes a link quality prediction technique for wireless networks.…”

Section: Link Quality Predictionmentioning

confidence: 99%

Alternative topology construction for cooperative data distribution in mobile ad hoc networks

Cvjetkovic

Rakocevic

2016

2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Ad hoc networks of mobile nodes can be used to extend or improve connectivity, and cooperative data distribution represents the basis of this approach. Cooperation in such networks can be improved by providing nodes with information on network dynamics and topology changes. This paper proposes a proactive approach to handling mobility-induced network topology changes. The approach uses signal strength trends to predict the future locations and connectivity between the network nodes. Our research is aimed towards the creation of an alternative topology in each node, where a node would keep other nodes suitability for cooperation in the data distribution. In this paper we present the initial algorithm and test the prediction method using a simulation. The algorithm is then applied in an experimental testbed where its performance was tested using real moving nodes executing a real data distribution process. The performance results show a significant improvement in terms of file transfer delay. Permanent repository link

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Section: Link Quality Predictionmentioning

confidence: 99%

Alternative topology construction for cooperative data distribution in mobile ad hoc networks

Cvjetkovic

Rakocevic

2016

2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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“…In the literature, link models typically reflect two approaches: physical models that use signal propagation to explain packet loss, and data-driven models that mathematically capture those effects with statistics like correlation between packet receptions. The former group includes distance-based attenuation, two-ray interference, and geometric occlusion models [11,12,13], while the latter includes loss-rate and Markov-chain models that reproduce distributions of consecutive receptions [14,15,16]. Though physics-driven methods are intuitive, they require detailed environment-specific information as inputs.…”

Section: Predictive Link-state Modelingmentioning

confidence: 99%

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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“…But the model is too computationally intensive to be implemented on a wireless sensor node. Farakas et al [6] apply cross-correlation and a pattern matching algorithm to predict link quality variations. The combined computation cost of correlation and pattern matching makes the technique likewise expensive.…”

Section: Mobility Estimationmentioning

confidence: 99%

A mobility-aware medium access control protocol for wireless sensor networks

Tang

Dargie

2010

2010 IEEE Globecom Workshops

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Abstract-This paper introduces a light-weight mobility-aware medium access control protocol for wireless sensor networks (MA-MAC). In many respects, the protocol is similar to preamble based, low power listening MAC protocols. It defines a dutycycle to let nodes sleep when they have no packets to transmit. During active communication, the protocol estimates mobility by evaluating the received signal strength of incoming ACK packets and initiates seamless handover by embedding neighbour discovery requests in data packets. Neighbour nodes randomly wake up from their sleep to participate in a handover process. Moreover, at the beginning of each active cycle, they first listen for handover request before they transmit. A prototype was developed and tested with TinyOS and Micaz platforms. The paper reports the experiment results.

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Link quality prediction in mesh networks

Cited by 75 publications

References 15 publications

Alternative topology construction for cooperative data distribution in mobile ad hoc networks

Alternative topology construction for cooperative data distribution in mobile ad hoc networks

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

A mobility-aware medium access control protocol for wireless sensor networks

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