Application of Machine-Learning Based Prediction Techniques in Wireless Networks

Gitanjali Bhutani

doi:10.4236/ijcns.2014.75015

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International Journal of Communications,... > Vol.7 No.5, May 2014

Application of Machine-Learning Based Prediction Techniques in Wireless Networks ()

Gitanjali Bhutani

WCDMA, Alcatel-Lucent Technologies India Private Limited, Bangalore, India.

DOI: 10.4236/ijcns.2014.75015 PDF HTML XML 4,910 Downloads 7,198 Views Citations

Abstract

Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.

Keywords

Wireless Networks, Prediction, Machine Learning, Ubiquitous Communication, Pervasive Computing

Share and Cite:

Bhutani, G. (2014) Application of Machine-Learning Based Prediction Techniques in Wireless Networks. International Journal of Communications, Network and System Sciences, 7, 131-140. doi: 10.4236/ijcns.2014.75015.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Bhutani, G. (2010) A Near-Optimal Scheme for TCP ACK Pacing to Maintain Throughput in Wireless Networks. Proceedings of the 2nd International Conference on Communication Systems and Networks, Bangalore, January 2010, 491-497.
[2]	Liu, T. and Cerpa, A.E. (2011) Foresee (4C): Wireless Link Prediction Using Link Features. 2011 10th International Conference on Information Processing in Sensor Networks (IPSN), Chicago, 12-14 April 2011, 294-305.
[3]	Fonseca, R., Gnawali, O., Jamieson, K. and Levis, P. (2007) Four-Bit Wireless Link Estimation. Proceedings of the Sixth Workshop on Hot Topics in Networks (HotNets VI), Atlanta, 14-15 November, 2007.
[4]	Alizai, M.H., Landsiedel, O., Link, J.A.B., Gotz, S. and Wehrle, K. (2009) Bursty Traffic over Bursty Links. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, New York, 4-6 November 2009, 71-84.
[5]	Liu, H., Al-Khafaji, S.K. and Smith, A.E. (2011) Prediction of Wireless Network Connectivity Using a Taylor Kriging Approach. International Journal of Advanced Intelligence Paradigms, 3, 112-121. http://dx.doi.org/10.1504/IJAIP.2011.039744
[6]	Konak, A. (2009) A Kriging Approach to Predicting Coverage in Wireless Networks. International Journal of Mobile Network Design and Innovation, 3, 65-71. http://dx.doi.org/10.1504/IJMNDI.2009.030838
[7]	Capka, J. and Boutaba, R. (2004) Mobility Prediction in Wireless Networks Using Neural Networks. Management of Multimedia Networks and Services, 3271, 320-333. http://dx.doi.org/10.1007/978-3-540-30189-9_26
[8]	Prasad, P.S. and Agrawal, P. (2010) Movement Prediction in Wireless Networks Using Mobility Traces. 7th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, 9-10 January 2010, 1-5.
[9]	Prasad, P.S. and Agrawal, P. (2009) Mobility Prediction for Wireless Network Resource Management. 41st Southeastern Symposium on System Theory, Tullahoma, 15-17 March 2009, 98-102.
[10]	Crawdad: Wireless Traces from Dartmouth. http://crawdad.cs.dartmouth.edu/
[11]	Wanalertlak, W., Lee, B., Yu, C., Kim, M., Park, S.M. and Kim, W.T. (2011) Behavior-Based Mobility Prediction for Seamless Handoff in Mobile Wireless Networks. Wireless Networks, 17, 645-658. http://dx.doi.org/10.1007/s11276-010-0303-x
[12]	Pahal, S., Singh, B. and Arora, A. (2013) A Prediction Based Handover Trigger in Overlapped Heterogeneous Wireless Networks. 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC), Solan, 26-28 September 2013, 1-6.
[13]	Yan, J., Zhao, L. and Li, J. (2011) A Prediction-Based Handover Trigger Time Selection Strategy in Varying Network Overlapping Environment. 2011 IEEE Vehicular Technology Conference (VTC Fall), San-Francisco, 5-8 September 2011, 1-5.
[14]	Wang, Q. and Ali Abu-Rgheff, M. (2003) A Multi-Layer Mobility Management Architecture Using Cross-Layer Signalling Interactions. 5th European Personal Mobile Communications Conference, Glasgow, 22-25 April 2003, 237-241.
[15]	Yoo, S.J., Cypher, D. and Golmie, N. (2010) Timely Effective Handover Mechanism in Heterogeneous Wireless Networks. Wireless Personal Communications, 52, 449-475. http://dx.doi.org/10.1007/s11277-008-9633-8
[16]	Salih, Y.K., See, O.H. and Yussof, S. (2012) A Fuzzy Predictive Handover Mechanism Based on MIH Links Triggering in Heterogeneous Wireless Networks. International Proceedings of Computer Science & Information Technology, 41, 225.
[17]	Liang, X., Li, X., Shen, Q., Lu, R., Lin, X., Shen, X. and Zhuang, W. (2012) Exploiting Prediction to Enable Secure and Reliable Routing in Wireless Body Area Networks. 2012 Proceedings IEEE INFOCOM, 25-30 March 2012, 388-396.
[18]	Guan, Q., Yu, F.R., Jiang, S. and Wei, G. (2010) Prediction-Based Topology Control and Routing in Cognitive Radio Mobile Ad Hoc Networks. IEEE Transactions on Vehicular Technology, 59, 4443-4452. http://dx.doi.org/10.1109/TVT.2010.2069105
[19]	Alavi, B. and Pahlavan, K. (2006) Modeling of the TOA-Based Distance Measurement Error Using UWB Indoor Radio Measurements. Communications Letters, 10, 275-277. http://dx.doi.org/10.1109/LCOMM.2006.1613745
[20]	Ravi, R.J. and PonLakshmi, R. (2013) A New Lifetime Prediction Algorithm Based Routing for VANETs. International Journal of Computer Science & Applications (TIJCSA), 1, 72-78.
[21]	Sharma, A.K. and Parihar, P.S. (2013) An Effective DoS Prevention System to Analysis and Prediction of Network Traffic Using Support Vector Machine Learning. International Journal of Application or Innovation in Engineering & Management, 2, 249-256.
[22]	Wu, J., Liu, S., Zhou, Z. and Zhan, M. (2012) Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning. International Journal of Distributed Sensor Networks, 2012, 243841. http://dx.doi.org/10.1155/2012/243841
[23]	Maei, H.R., Szepesvari, C., Bhatnagar, S., Precup, D., Silver, D. and Sutton, R.S. (2009) Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS’09), Vancouver, 7-10 December 2009.
[24]	Eik Loo, C., Yong Ng, M., Leckie, C. and Palaniswami, M. (2006) Intrusion Detection for Routing Attacks in Sensor Networks. International Journal of Distributed Sensor Networks, 2, 313-332. http://dx.doi.org/10.1080/15501320600692044
[25]	Chen, C., Ma, J. and Yu, K. (2006) Designing Energy-Efficient Wireless Sensor Networks with Mobile Sinks. Proceeding of the 4th ACM Conference on Embedded Networked Sensor Systems (SenSys 2006), Colorado, 31 October-3 November 2006.
[26]	Yan, K.Q., Wang, S.C. and Liu, C.W. (2009) A Hybrid Intrusion Detection System of Cluster-Based Wireless Sensor Networks. Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, 18-20 March 2009, 18-20.
[27]	Shen, W., Han, G., Shu, L., Rodrigues, J.J. and Chilamkurti, N. (2012) A New Energy Prediction Approach for Intrusion Detection in Cluster-Based Wireless Sensor Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 51, 1-12.