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Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming

Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming

J. Sangeetha, Keerthiraj Nagaraj, K. N. Balasubramanya Murthy, Ram P. Rustagi
Copyright: © 2018 |Pages: 37
ISBN13: 9781522541516|ISBN10: 1522541519|EISBN13: 9781522541523
DOI: 10.4018/978-1-5225-4151-6.ch002
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MLA

Sangeetha, J., et al. "Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming." Advancements in Applied Metaheuristic Computing, edited by Nilanjan Dey, IGI Global, 2018, pp. 34-70. https://doi.org/10.4018/978-1-5225-4151-6.ch002

APA

Sangeetha, J., Nagaraj, K., Murthy, K. N., & Rustagi, R. P. (2018). Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming. In N. Dey (Ed.), Advancements in Applied Metaheuristic Computing (pp. 34-70). IGI Global. https://doi.org/10.4018/978-1-5225-4151-6.ch002

Chicago

Sangeetha, J., et al. "Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming." In Advancements in Applied Metaheuristic Computing, edited by Nilanjan Dey, 34-70. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-4151-6.ch002

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Abstract

The WiMAX network provides an efficient QoS to the large number of users. The real time and non-real time multimedia applications are gaining importance in the WiMAX network. To support such applications, there is a need to propose an efficient QoS of traffic prediction mechanism for the WiMAX networks. To address this, the authors have applied Gene Expression Programming technique for CBR based traffic and file transfer applications in the WiMAX network. The main focus of this chapter is to develop the mathematical expressions for throughput of the network by considering bandwidth, average end-to-end delay and average jitter as inputs for CBR based traffic and file transfer applications. This expression helps to analyze and predict the QoS of traffic of a given network. The simulation results show that the model values and the target values match with better approximation. Experimentally GEP performs better than other existing algorithms. Furthermore, sensitivity analysis has been carried out for both the applications

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