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Related Experiment Video

Updated: Jan 14, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Fuzzy soft tensor based group decision making approach with application to heterogeneous wireless network evaluation.

Muhammad Bilal¹, Li Chaoqian², Ioan Lucian Popa^3,4

¹School of Mathematics and Statistics, Yunnan University, Kunming, 650106, China. bilalmaths28@gmail.com.

Scientific Reports

|October 17, 2025

Summary

This study presents a Fuzzy Soft Tensor (FST) model for complex group decisions. The FST framework effectively handles uncertainty and imprecise data, identifying 5G NR as the optimal choice in wireless network selection.

Keywords:

Fuzzy soft set Fuzzy soft tensor Group decision making Heterogeneous wireless network Soft set

Related Experiment Videos

Last Updated: Jan 14, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Decision Sciences
Artificial Intelligence
Information Systems

Background:

Complex group decision-making often involves uncertainty and imprecise expert judgments.
Existing multi-criteria decision-making (MCDM) models struggle with integrating diverse, vague, and inconsistent data.
There is a need for robust frameworks that can model expert knowledge across multiple dimensions.

Purpose of the Study:

To introduce a novel Fuzzy Soft Tensor (FST) group decision-making framework.
To develop an aggregation-driven algorithm for combining expert evaluations within the FST model.
To demonstrate the framework's applicability and robustness in a real-world heterogeneous wireless network selection scenario.

Main Methods:

Integration of fuzzy set theory and soft set theory within a multidimensional tensorial structure.
Development of a new aggregation-driven algorithm for systematic combination of expert evaluations.
Application of the FST framework to evaluate six wireless network technologies against six performance criteria.

Main Results:

The FST-based approach successfully identified 5G NR as the most suitable network alternative.
Results showed strong agreement with established MCDM methods (TOPSIS, GRA, MOORA, WASPAS).
The FST model demonstrated improved handling of vague, inconsistent, and multi-perspective data with computational efficiency.

Conclusions:

The proposed FST framework is a scalable and reliable decision-support tool for complex, dynamic environments.
The FST model offers a flexible approach to modeling expert knowledge and handling uncertainty in group decision-making.
This research validates the effectiveness of the FST approach in practical applications like network selection.