Hidden Markov Model Traffic Characterisation in Wireless Networks

Evangelos D. Spyrou, Dimitris Mitrakos

Abstract

Quality of service wireless traffic that often exhibits burstiness, occasionally occurring due to mobility, provides a critical networking issue. Traffic patterns in wireless networks are not of a traditional nature. Nodes transmit their information in batches over a short period of time, before they lose connection. Prediction of wireless incoming load plays an important role in the design of wireless local area networks. The issues of load balancing and Quality of Service constraints are a major problem, which is responsible for the increase of throughput of the network; thus, predicting traffic can be of a great assistance in the aforementioned research directions, leading to a significant optimisation of the wireless network operation. This paper addresses the problem of traffic prediction using Hidden Markov Models. The data is clustered using the Information Based Similarity index that classifies different types of traffic. We show the limitation of this approach and we finally select Euclidean distance for data clustering. Together, they provide an efficient solution towards the solution of wireless traffic characterisation and prediction. We show the efficiency of our scheme in a series of simulations

References

  1. Alizai, M. H., Landsiedel, O., Link, J. Í . B., Götz, S.,and Wehrle, K. (2009). Bursty traffic over bursty links. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pages 71-84. ACM.
  2. Beran, J. (1994). Statistics for long-memory processes, Volume 61. CRC press.
  3. Dunham, M. H., Meng, Y., and Huang, J. (2004).Extensible markov model. In Data Mining, 2004. ICDM'04. Fourth IEEE International Conference on, pages 371- 374. IEEE.
  4. Eddy, S. R. (1996). Hidden markov models. Current opinion in structural biology, 6(3):361-365.
  5. Jiang, H. and Dovrolis, C. (2005). Why is the internettraffic bursty in short time scales? In ACM SIGMETRICS Performance Evaluation Review, volume 33, pages 241-252. ACM. Jiang, M.,
  6. Nikolic, M., Hardy, S., and Trajkovic, L. (2001).Impact of self-similarity on wireless data network performance. In Communications, 2001. ICC 2001. IEEE International Conference on, volume 2, pages 477- 481. IEEE.
  7. Kosko, B. (1992). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence/ book and disk. Prentice Hall, Upper Saddle River.
  8. Li, R., Zhao, Z., Zhou, X., Palicot, J., and Zhang, H. (2014). The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice. IEEE Communications Magazine, 52(6):234- 240.
  9. Loumiotis, I., Adamopoulou, E., Demestichas, K.,Kosmides, P., and Theologou, M. (2014). Artificial neural networks for traffic prediction in 4g networks. In International Wireless Internet Conference, pages 141- 146. Springer.
  10. Maheshwari, S., Mahapatra, S., Kumar, C. S., and Vasu,K. (2013). A joint parametric prediction model for wireless internet traffic using hidden markov model. Wireless networks, 19(6):1171-1185.
  11. MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
  12. Ni, F., Zang, Y., and Feng, Z. (2015). A study on cellular wireless traffic modeling and prediction using elman neural networks. In 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), volume 1, pages 490-494. IEEE.
  13. Pan, T., Sumalee, A., Zhong, R.-X., and Indra-Payoong,N. (2013). Short-term traffic state prediction based on temporal-spatial correlation. IEEE Transactions on Intelligent Transportation Systems, 14(3):1242-1254.
  14. Papadopouli, M., Shen, H., Raftopoulos, E., Ploumidis,M., and Hernandez-Campos, F. (2005). Short-term traffic forecasting in a campus-wide wireless network. In 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications, volume 3, pages 1446-1452. IEEE.
  15. Papadopoulos, G. Z., Kotsiou, V., Gallais, A.,Chatzimisios, P., and Nöel, T. (2015). Wireless medium access control under mobility and bursty traffic assumptions in wsns. Mobile Networks and Applications, 20(5):649- 660.
  16. Park, K. and Willinger, W. (2000). Self-similar network traffic and performance evaluation. Wiley Online Library.
  17. Rabiner, L. and Juang, B. (1986). An introduction to hidden markov models. ieee assp magazine, 3(1):4-16.
  18. Rutka, G. and Lauks, G. (2015). Study on internet traffic prediction models. Elektronika ir Elektrotechnika, 78(6):47-50.
  19. Spyrou, E. D. and Mitrakos, D. K. (2015). On the Homogeneous transmission power under the sinr model. In 2015 ICTRS. SCITEPRESS.
  20. Yadav, R. K. and Balakrishnan, M. (2014). Comparative evaluation of arima and anfis for modeling of wireless network traffic time series. EURASIP Journal on Wireless Communications and Networking, 2014(1): 1-8.
  21. Yang, A. C.-C., Hseu, S.-S., Yien, H.-W., Goldberger, A.L., and Peng, C.-K. (2003). Linguistic analysis of the human heartbeat using frequency and rank order statistics. Physical review letters, 90(10):108103.
Download


Paper Citation


in Harvard Style

Spyrou E. and Mitrakos D. (2016). Hidden Markov Model Traffic Characterisation in Wireless Networks . In Proceedings of the Fifth International Conference on Telecommunications and Remote Sensing - Volume 1: ICTRS, ISBN 978-989-758-200-4, pages 78-85. DOI: 10.5220/0006227400780085


in Bibtex Style

@conference{ictrs16,
author={Evangelos D. Spyrou and Dimitris Mitrakos},
title={Hidden Markov Model Traffic Characterisation in Wireless Networks},
booktitle={Proceedings of the Fifth International Conference on Telecommunications and Remote Sensing - Volume 1: ICTRS,},
year={2016},
pages={78-85},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006227400780085},
isbn={978-989-758-200-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Telecommunications and Remote Sensing - Volume 1: ICTRS,
TI - Hidden Markov Model Traffic Characterisation in Wireless Networks
SN - 978-989-758-200-4
AU - Spyrou E.
AU - Mitrakos D.
PY - 2016
SP - 78
EP - 85
DO - 10.5220/0006227400780085