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Wang, X, Qu, D, Li, K, Cheng, H, Das, SK, Huang, M, Wang, R and Chen, S (2017) A flexible and generalized framework for access network selection in heterogeneous wireless networks. Pervasive and Mobile Computing, 40. pp. 556-576. ISSN 1574-1192
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A flexible and generalized framework for access network selection in heterogeneous wireless networks.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (966kB) | Preview |
Abstract
The rapid development and integration of heterogeneous wireless networks provide ubiquitous communications for mobile users. The intelligent and multimodal mobile terminals should select the best access network at any time anywhere. However, the “best” is a complex and fuzzy concept, which has different meanings to different users and even to the same user under different conditions. There are various factors to consider when deciding which one is the best for a mobile terminal. In this paper, we design a generalized and flexible framework for the access network selection in heterogeneous wireless networks. The framework is generalized because it considers various factors in a comprehensive way to get the solutions. These factors can be classified as network-related or user-related, economic or non-economic, objective or subjective, accurate or fuzzy. Meanwhile, the framework is also flexible because these factors can be customized and adapted to specific solutions. Under the framework, given Nmobile terminals and M access networks, we have developed a novel access network selection scheme based on a Quantum-inspired Immune Clonal Algorithm (QICA). Experimental results demonstrate that our proposed scheme provides better utilities f or both the users and the access networks, and also better services for users as compared with four other schemes. © 2017 Elsevier B.V.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0805 Distributed Computing, 1702 Cognitive Science |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Elsevier |
| Date Deposited: | 04 Oct 2017 10:45 |
| Last Modified: | 21 Mar 2022 11:14 |
| DOI or ID number: | 10.1016/j.pmcj.2017.01.001 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/7270 |
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