A Comparative Evaluation of QoS-Based Network Selection Between TOPSIS and VIKOR

Malathy, E. M.; Muthuswamy, Vijayalakshmi

doi:10.1007/978-981-13-2354-6_13

Cited by 4 publications

(2 citation statements)

References 10 publications

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Section: Analytical Hierarchy Process and Grey Relational Analysismentioning

confidence: 99%

“…Several MADM methods, such as VIKOR, 27 SWARM, 28 and ELECTRE, 29 have been utilized in the network selection problem. Nevertheless, our proposal was not compared to alternative approaches such as GA, GT fuzzy logic, utility functions for various reasons: While some of these methods are advanced and involve complex computations, they are capable of achieving results comparable to the previously discussed approaches. It is worth noting that this article does not aim to provide a comprehensive survey or review, and as a result, it is not possible to describe all the techniques employed for addressing the network selection problem. …”

Section: Network Selection Problemmentioning

confidence: 99%

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A machine learning access network selection in a heterogeneous wireless environment

Bendaoud,

Abdennebi

2023

Concurrency and Computation

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In the context of Heterogeneous Wireless Networks (HWN), achieving the best connectivity is crucial for users. With the availability of multiple Radio Access Technologies (RATs), users are constantly looking to be “Always Best Connected” (ABC) by connecting to the best possible RAT. This challenge is also important in Next‐Generation Networks (NGN), where researchers are proposing models and algorithms to make the ABC paradigm a reality. One such challenge is the network selection problem, which involves the automatic and transparent selection of the best RAT at a given time. Many papers in this field have used Multi‐Attribute Decision‐Making (MADM) methods, which have been implemented with various objectives such as increasing quality of service (QoS), reducing energy consumption and costs etc. This paper proposes a modified machine learning algorithm, specifically an adapted K‐means algorithm, to address this challenge. The results show that this proposal outperforms legacy MADM methods in terms of latency, jitter, packet loss, and data rate. So, the paper presents a new approach to network selection in HWN and NGN, and the results suggest that it could be a promising solution to this important challenge.

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Section: Analytical Hierarchy Process and Grey Relational Analysismentioning

confidence: 99%

Section: Network Selection Problemmentioning

confidence: 99%

A machine learning access network selection in a heterogeneous wireless environment

Bendaoud,

Abdennebi

2023

Concurrency and Computation

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Prioritizing Python Code Smells for Efficient Refactoring Using Multi-criteria Decision-Making Approach

Gupta

Sharma

Phulli

2021

Advances in Intelligent Systems and Computing

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A Multi-label Feature Selection Method Based on Multi-objectives Optimization by Ratio Analysis

Verma,

Sahu

2023

Lecture Notes in Networks and Systems

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A Comparative Evaluation of QoS-Based Network Selection Between TOPSIS and VIKOR

Cited by 4 publications

References 10 publications

A machine learning access network selection in a heterogeneous wireless environment

A machine learning access network selection in a heterogeneous wireless environment

Prioritizing Python Code Smells for Efficient Refactoring Using Multi-criteria Decision-Making Approach

A Multi-label Feature Selection Method Based on Multi-objectives Optimization by Ratio Analysis

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