Mobility Aware Network Selection in a Heterogeneous Wireless Environment

Author:

Bendaoud Fayssal1,Abdennebi Marwen2,Didi Fedoua3

Affiliation:

1. LabRI-SBA Lab , Ecole supérieure en Informatique, Sidi Bel Abbes , Algeria

2. L2T1 Lab , University of Paris 13 , France

3. LRIT Lab , University of Tlemcen , Algeria

Abstract

Abstract Different Radio Access Technologies (RATs) coexist in the same area has encouraged the researchers to get profit from the available networks by selecting of the best RAT at each moment of the call session to satisfy the user requirements. In this paper, we address a real-world problem which is the frequent mobility of the users in heterogeneous networks. We present in this paper a framework that allows users to select the best networks for the whole call session especially form a mobility perspective. The framework consists of several steps, starting by the path prediction which is performed using a Markov model order 2. The second step is to make the network selection on the zones of each predicted path, while in the third step; we get the best RAT’s configuration for each predicted path. Finally, we use another function to select one of the best configurations to be used for all the possible used paths. The results show that our proposal performs very well by eliminating the unnecessary vertical handover while maintaining a good Quality of Service (QoS).

Publisher

Walter de Gruyter GmbH

Subject

Computer Science Applications,General Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A machine learning access network selection in a heterogeneous wireless environment;Concurrency and Computation: Practice and Experience;2023-12-19

2. A modified k-means algorithm for network selection in heterogeneous wireless environment;2023 IEEE International Conference on Networking, Sensing and Control (ICNSC);2023-10-25

3. Interface management in multi-interface mobile communication: a technical review;International Journal of System Assurance Engineering and Management;2022-01-26

4. Adaptive Knn-based algorithm for network selection in next-generation networks;Journal of High Speed Networks;2021-11-10

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