Normal wiggly hesitant fuzzy modelling approach for 6G frameworks based blockchain technology

【Author】 Qahtan, Sarah; Mourad, Nahia; Ibrahim, Hassan A.; Zaidan, Aws Alaa; Bahaa, Bilal; Deveci, Muhammet; Ding, Weiping; Pamucar, Dragan; Pedrycz, Witold; Shamini, Saraswathy

【Source】EXPERT SYSTEMS WITH APPLICATIONS

【影响因子】8.665

【Abstract】The use of blockchain technology in sixth-generation (6G) networks promises substantial improvements in security, privacy, efficiency, and cost-effectiveness. Extensive research has explored various 6G frameworks incorporating blockchain, revealing how different blockchain features impact technical requirements. Thus, selecting and modelling these frameworks are pivotal. A research gap exists as no standard model exists for these frameworks based on blockchain features, presenting a multi-attribute decision-making challenge due to feature diversity, uncertain importance, and data variability. This study introduces an innovative intelligent approach, incorporating fuzzy weighted zero inconsistency (FWZIC) based on the interrelationship process under a normal wiggly hesitant fuzzy set (NWHFS) environment named NWHFS-FWZIC method and ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI) method. The decision matrix for the study is built by intersecting 10 6G technical requirements with 7 blockchain evaluation features of 6G frameworks. The proposed intelligent approach in NWHFS-FWZIC is further developed to ascertain the importance level of the multiple blockchain features. These data are used in RAFSI. Results revealed that the decentralization feature (Dec) was assigned the highest weight value (0.4653) according to NWHFS-FWZIC, indicating its important role in modelling 6G frameworks. The non-repudiation feature (NR) was assigned a weight value of 0.0156, indicating its moderate importance in modelling 6G frameworks. ERLLC/eURLLC achieved the highest score of 0.7600, reflecting strong performance across most features, particularly in NR, Dec, and Smart Contracts (SCs). LDHMC followed closely with a score of 0.7433, demonstrating balanced performance with notable strengths in Immutability (Imm) and Trust-building (TB). The FeMBB and umMTC models also performed well, with scores of 0.7106 and 0.7223 respectively, indicating reliability and efficiency in multiple evaluation features. while ELPC and HATC were the least favoured frameworks. The robustness of the proposed method is assessed through sensitivity analyses, systematic test, and performance comparative with state of art study.

【Keywords】Blockchain technology; FWZIC; Multi-criteria decision-making; Normal wiggly hesitant fuzzy set; RAFSI; 6G

【发表时间】2025 JAN 1

【收录时间】2024-09-18

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-6G