TOPSIS-based dynamic approach for mobile network interface selection

Abstract : The rapid evolution in mobile wireless communication networks has generated Heterogeneous Wireless Networks (HWNs), which cover a diverse range of networks (e.g., 2G, 3G, and LTE-A). In HWNs, a mobile device supports multiple network interfaces that use different access methods for wireless links. In such an environment, the main challenge is Always Best Connected (ABC), which means that the mobile nodes rank the network interfaces and select the best one at anytime and anywhere according to multiple criteria (application-related criteria, network-related criteria, terminal-related criteria, user-related criteria). In this context, Multi Attribute Decision Making (MADM) techniques present a promising solution for the network interface selection problem. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one widely adopted MADM method. TOPSIS suffers from ranking abnormalities, e.g., if a low-ranking network (alternative) is disconnected or a new network is discovered, then the order of the higher-ranking networks will change abnormally. These abnormalities can potentially decrease the quality of the results. In this paper, we propose new TOPSIS-based approaches for network interface selection that efficiently tackle the ranking abnormality problem in HWNs. The performance of our methods is evaluated through simulations. The results show that the proposed approaches reduce or completely eliminate the rank reversal, either when networks are disconnected or new networks are connected.
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Elsevier Computer Networks, Elsevier, 2016, 107 (2), pp.304-314
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https://hal.archives-ouvertes.fr/hal-01490434
Contributeur : Muhammad Sajid Mushtaq <>
Soumis le : mercredi 15 mars 2017 - 12:33:14
Dernière modification le : jeudi 16 mars 2017 - 01:04:33

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  • HAL Id : hal-01490434, version 1

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Mohamed Abdelkrim Senouci, Muhammad Sajid Mushtaq, Said Hoceini, Abdelhamid Mellouk. TOPSIS-based dynamic approach for mobile network interface selection. Elsevier Computer Networks, Elsevier, 2016, 107 (2), pp.304-314. 〈hal-01490434〉

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