A Prediction Model For Performance Analysis in Wireless Mesh Networks

Yıl 2016, Cilt: 6 Sayı: 3, 1241 - 1250, 05.11.2016

Şafak Durukan Odabaşı Ergün Gümüş

Öz

Analysis of computer networks is an important
study field that must be handled carefully in order to make communication
systems work properly. Efficient evaluation and remodelling of system according
to factors affecting the performance is required. For this aim, many techniques
have been proposed, so far. However, machine learning methods are getting more
preferable than others with their cost-effective and faster solutions. In this
study, generalized regression neural networks (GRNNs) approach was employed in
order to predict the output, packets dropped of a sample DMesh network
simulation. The simulation is driven by parameters such as number of nodes,
number of gateways, number of channels used, and traffic density. It was
observed that parameters: traffic density and number of channels used, have a
direct impact on error rate of the regression model. The high variance
explained values show that GRNN approach can represent real characteristics of
DMesh architecture.

Anahtar Kelimeler

Mesh networks, source management, prediction model, generalized regression neural networks, performance analysis

Kaynakça

• Rappaport T.S., Wireless communications: principles and practice (2nd edition). Upper Saddle River, NJ: Prentice Hall PTR. ISBN 0-13-042232-0, 2002.
• Dai L., Yang W., Gao S., Xia Y., Zhu M., and Ji Z., “EMD-based multi-model prediction for network traffic in software-defined networks,” in IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Philadelphia, PA, pp. 539-544, 2014.
• Das S.M., Pucha H., Koutsonikolas D., Hu C., and Peroulis D., “Dmesh: Incorporating practical directional, antennas in multi-channel wireless mesh networks,” in IEEE J. Sel. Areas Commun., vol. 24, no. 11, pp. 2028-2039, 2006.
• Ahmed N.M., and Chen L., “An efficient algorithm for link prediction in temporal uncertain social networks,” Information Sciences, vol. 331, pp. 120-136, 2016.
• Wu M., Tan L., and Xiong N., “Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications,” Information Sciences, vol. 329, pp. 800-818, 2016.
• Priya S.B.M., “Adaptive control of routing protocol in mobile adhoc network using regression model,” in International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET), Tiruchirappalli, Tamilnadu, India, vol. 13-14, pp. 509-514, 2012.
• Gu C., Zhang S., Xue X., and Huang H., “Online wireless mesh network traffic classification using machine learning,” Journal of Computational Information Systems, vol. 7, no. 5, pp. 1524-1532, 2011.
• Alzubir A., Bakar K.A., Yousif A., and Abuobieda A., “State of the art, channel assignment multi-radio multi-channel in wireless mesh network,” International Journal of Computer Applications, vol. 37, no. 4, pp. 14-20, 2012.
• Riggio R., Pellegrini F.D., Miorandi D., and Chlamtac I., “A knowledge plane for wireless mesh networks,” Ad Hoc & Sensor Wireless Networks, vol. 5, pp. 293-311, 2007.
• Yarali A., Ahsant B.., and Rahman S., “Wireless mesh networking: a key solution for emergency&rural applications,” in IEEE Second International Conference on Advances in Mesh Networks, pp. 143-149, 2009.
• Draves R., Padhye J., and Zill B., “Comparison of routing metrics for static multi-hop wireless networks,” in Conference on Applications, technologies, architectures, and protocols for computer communications (SIGCOMM), pp. 133-144, 2004.
• Raniwala A., and Chiueh T., “Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network,” in 24th Annual Joint Conference of the IEEE Computer and Communications Societies, New York, pp. 2223-2234, 2005.
• Received from: https://tools.ietf.org/html/rfc3626
• Zurada J.M., Introduction to artificial neural systems. West Publishing Co., St. Paul, MN, USA, 1992.
• Specht D.F., “A general regression neural network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568-576, 1991.
• Wasserman P.D., Advanced methods in neural computing. New York, Van Nostrand Reinhold, pp. 155-61, 1993.
• MATLAB Release 2015a, The MathWorks, Inc., Natick, Massachusetts, United States.