Balancing Subjectivity and Objectivity in Network Selection: A Decision-Making Framework Towards Digital Twins
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908296247508992 |
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| author | Mefgouda, Brahim Idoudi, Hanen Al-Quraan, Mohammad Lotfi, Ismail Alhussein, Omar Mohjazi, Lina Muhaidat, Sami |
| author_facet | Mefgouda, Brahim Idoudi, Hanen Al-Quraan, Mohammad Lotfi, Ismail Alhussein, Omar Mohjazi, Lina Muhaidat, Sami |
| contents | Selecting the optimal radio access technology (RAT) during vertical handovers (VHO) in heterogeneous wireless networks (HWNs) is critical. Multi-attribute decision-making (MADM) is the most common approach used for network selection (NS) in HWNs. However, existing MADM-NS methods face two major challenges: the rank reversal problem (RRP), where the relative ranking of alternatives changes unexpectedly, and inefficient handling of user and/or service requirements. These limitations result in suboptimal RAT selection and diminished quality of service, which becomes particularly critical for time-sensitive applications. To address these issues, we introduce in this work a novel weighting assignment technique called BWM-GWO, which integrates the Best-Worst Method (BWM) with the Grey Wolf Optimization (GWO) algorithm through a convex linear combination. The proposed framework achieves a balanced decision-making process by using BWM to compute subjective weights that capture user/service preferences, while employing GWO to derive objective weights aimed at minimizing RRP. The development and validation of this framework establish a digital model for NS in HWNs, marking the initial step toward realizing a digital twin (DT). Experimental results show that integrating the proposed BWM-GWO technique with MADM-NS reduces RRP occurrence by up to 71.3% while significantly improving user and service satisfaction compared to benchmark approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_01414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Balancing Subjectivity and Objectivity in Network Selection: A Decision-Making Framework Towards Digital Twins Mefgouda, Brahim Idoudi, Hanen Al-Quraan, Mohammad Lotfi, Ismail Alhussein, Omar Mohjazi, Lina Muhaidat, Sami Networking and Internet Architecture Selecting the optimal radio access technology (RAT) during vertical handovers (VHO) in heterogeneous wireless networks (HWNs) is critical. Multi-attribute decision-making (MADM) is the most common approach used for network selection (NS) in HWNs. However, existing MADM-NS methods face two major challenges: the rank reversal problem (RRP), where the relative ranking of alternatives changes unexpectedly, and inefficient handling of user and/or service requirements. These limitations result in suboptimal RAT selection and diminished quality of service, which becomes particularly critical for time-sensitive applications. To address these issues, we introduce in this work a novel weighting assignment technique called BWM-GWO, which integrates the Best-Worst Method (BWM) with the Grey Wolf Optimization (GWO) algorithm through a convex linear combination. The proposed framework achieves a balanced decision-making process by using BWM to compute subjective weights that capture user/service preferences, while employing GWO to derive objective weights aimed at minimizing RRP. The development and validation of this framework establish a digital model for NS in HWNs, marking the initial step toward realizing a digital twin (DT). Experimental results show that integrating the proposed BWM-GWO technique with MADM-NS reduces RRP occurrence by up to 71.3% while significantly improving user and service satisfaction compared to benchmark approaches. |
| title | Balancing Subjectivity and Objectivity in Network Selection: A Decision-Making Framework Towards Digital Twins |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2504.01414 |