Multi-Objective Whale Optimization for Network Interface Selection in HWNs

The network interface selection (NIS) is an important function that should be executed to connect the user's equipment to the best available network anytime and anywhere by meeting the user/application QoS requirements. Multi-attribute decision-making approaches (MADM) are commonly applied to m...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings - IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises s. 23 - 26
Hlavní autoři: Mefgouda, Brahim, Idoudi, Hanen
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2021
Témata:
5G mobile communication
Collaboration
Conferences
Heterogeneous wireless networks
Multi-Objective whale optimization algorithm
Network in-terface selection
Quality of service
Surface acoustic waves
Whale optimization algorithms
Wireless communication
ISSN:2641-8169
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The network interface selection (NIS) is an important function that should be executed to connect the user's equipment to the best available network anytime and anywhere by meeting the user/application QoS requirements. Multi-attribute decision-making approaches (MADM) are commonly applied to model and solve NIS problems. Although they can rank networks quickly with a high precision, they suffer from two drawbacks. Firstl, the rank reversal problem (RRP). Second, The selection of the highest-ranking score network without considering user and/or service requirements. In this paper, we introduce a novel approach to solve the above-cited drawbacks. We model the weighting techniques as a multi-objective problem. Then, we apply an extended version of the Whale optimization algorithm to obtain the decision criteria weights according to the traffic classes and the chosen MADM approaches. The obtained simulations clearly show that applying our approach with MADM approaches reduces the RRP and satisfies the user/service requirements.
ISSN:2641-8169
DOI:10.1109/WETICE53228.2021.00016