Link quality prediction in mesh networks

  • Authors:

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

  • Affiliations:

  • Institute of Informatics and Economics, University of West Hungary, Bajcsy-Zsilinszky u. 9., H-9400 Sopron, Hungary;Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Gloriastr. 35, CH-8092 Zurich, Switzerland;Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Gloriastr. 35, CH-8092 Zurich, Switzerland;Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Gloriastr. 35, CH-8092 Zurich, Switzerland;Center for Research in Wireless Mobility and Networking (CReWMaN), The University of Texas at Arlington, TX 76019, USA

  • Venue:
  • Computer Communications
  • Year:
  • 2008

Quantified Score

Hi-index 0.24

Visualization

Abstract

Wireless self-organizing networks such as mesh networks strive hard to get rid of mobility and radio propagation effects. Links - the basic elements ensuring connectivity in wireless networks - are impacted first from them. But what happens if one could mitigate these effects by forecasting the links' future states? In this paper, we propose XCoPred (using Cross-Correlation to Predict), a pattern matching based scheme to predict link quality variations. XCoPred does not require the use of any external hardware, it relies simply on Signal to Noise Ratio (SNR) measurements (that can be obtained from any wireless interface) as a quality measure. The nodes monitor and store the links' SNR values to their neighbors in order to obtain a time series of SNR measurements. When a prediction on the future state of a link is required, the node looks for similar SNR patterns to the current situation in the past (time series) using a cross-correlation function. The matches found are then used as a base for the prediction. Clearly, XCoPred takes advantage of the occurrence and recurrence of patterns observed in SNR measures reflecting the joint effect of human motion and radio propagation. XCoPred focuses only on the scale of links and as such is complementary to mobility prediction schemes, which target prediction at a broader scale. We first prove the occurrence of SNR patterns resulted by the joint effect of human motion and radio propagation. Then we evaluate XCoPred in an indoor mesh network showing, that XCoPred is able to recognize mobility patterns in up to 85% of the cases correctly and the average prediction error on mid-term predictions (i.e., assessing the future link quality more than 1min ahead) is less than half the error we get using linear prediction. Eventually, we propose and evaluate an enhanced handoff management scheme for 802.11 mesh networks showing the usefulness of XCoPred as a cross-layer input.