An Analytical Framework for Disconnection Prediction in Wireless Networks


The stability and reliability of links in wireless networks is dependent on a number of factors such as the topology of the area, inter-base station or inter-mobile station distances, weather conditions and so on. Link instability in wireless networks has a negative impact on the data throughput and thus, the overall quality of user experience, even in the presence of sufficient bandwidth. An estimation of link quality and link availability duration can drastically increase the performance of these networks, allowing the network or applications to take proactive measures to handle impending disconnections. In this paper we look at a mathematical model for predicting disconnection in wireless networks. This model is originally intended to be implemented in base stations of cellular networks, but is independent of the wireless technology and can thus be applied to different types of networks with minimum changes.

Share and Cite:

Bhutani, G. (2014) An Analytical Framework for Disconnection Prediction in Wireless Networks. International Journal of Communications, Network and System Sciences, 7, 165-174. doi: 10.4236/ijcns.2014.76018.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Goff, T., Moronski, J., Phatak, D. S. and Gupta, V. (2000) Freeze-TCP: A True End-to-End TCP Enhancement Mechanism for Mobile Environments. INFOCOM 2000. Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings, Tel-Aviv, 26-30 March 2000, 10 p.
[2] 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.
[3] Liang, B. and Haas, Z. J. (1999) Predictive Distance-Based Mobility Management for PCS Networks. INFOCOM’99. Proceedings of 18th Annual Joint Conference of the IEEE Computer and Communications Societies, New York, 21-25 March 1999, 1337-1384.
[4] Guerin, R.A. (1987) Channel Occupancy Time Distribution in a Cellular Radio System. IEEE Transactions on Vehicular Technology, 36, 89-99.
[5] Royer, E.M., Melliar-Smith, P.M. and Moser, L.E. (2001) An Analysis of the Optimum Node Density for ad Hoc Mobile Networks. IEEE International Conference on Communications, Helsinki, 11-14 June 2001, 5 p.
[6] Haas, Z.J. and Pearlman, M.R. (2001) The Performance of Query Control Schemes for the Zone Routing Protocol. IEEE/ACM Transactions on Networking (TON), 9, 427-438.
[7] Pearlman, M.R., Haas, Z.J., Sholander, P. and Tabrizi, S.S. (2000) On the Impact of Alternate Path Routing for Load Balancing in Mobile Ad Hoc Networks. 2000 1st Annual Workshop on Mobile and Ad Hoc Networking and Computing, Boston, 11 August 2000, 3-10.
[8] 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.
[9] 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.
[10] Konak, A. (2009) A Kriging Approach to Predicting Coverage in Wireless Networks. International Journal of Mobile Network Design and Innovation, 3, 65-71.
[11] Capka, J. and Boutaba, R. (2004) Mobility Prediction in Wireless Networks Using Neural Networks. Management of Multimedia Networks and Services, 3271, 320-333.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.