## 5 Conclusions, Limitations, and Future Directions of Research  
One of the most crucial IoT problems in real-world applications is object selection based on customer demand. Effective object selection, identification, and selection of relevant nodes are required to achieve optimal performance. As a result, experts may easily comprehend diverse views on IoT object selection methods. The findings of this work will also aid academics and provide insight into future research topics in this discipline. Furthermore, the disadvantages and benefits of the suggested method were examined, paving the way for the future development of more efficient and practical processes for object selection in IoT contexts. Therefore, fuzzy TOPSIS is a powerful approach for effective decision-making; however, the findings of this study may be validated by employing other tools, namely AHP, ANP, VIKOR, and ELECTRE, in the fuzzy environment. Moreover, a hybrid approach is used to validate and improve the accuracy of the results. In addition, after integrating the edge platform and blockchain into the IoT system, we divided this architecture into three layers: Edge in the center-connected IoT devices on behalf of an IoT domain at the bottom and cloud computing at the top. However, the IoT's deep integration of edge and blockchain technologies offers a new access control framework design approach. Therefore, this study proposes a unique node selection framework for blockchain-enabled edge IoT that implements a fast and reliable node selection technique. In addition, we  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**S.N** & \multicolumn{3}{c}{**INRE**} & \multicolumn{3}{c}{**INFBS**} & \multicolumn{3}{c}{**INDE**} \\ \hline
27 & 24.2 & 90 & 100 & 4.4 & 9 & 50 & 1.454 & 49.5 & 100 \\ \hline \(A^{*}\) & **27.225** & **100** & **100** & **4.95** & **10** & **50** & **1.4545** & **49.5** & **100** \\ \hline \(A^{-}\) & **0.55** & **1** & **11** & **0.1** & **0.1** & **5.5** & **0.1** & **1** & **5.454** \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**S.N** & \multicolumn{3}{c}{**Distance from FPIS**} & \({d_{i}}^{*}\) & \multicolumn{3}{c}{**Distance from FNIS**} & \({  
developed a hybrid MCDM model mechanism that can dynamically pick select nodes to obtain a rapid and reliable result using the fuzzy approach. In this study, three evaluation criteria, namely INRE, INFBS, and INDE, have been considered, and the Fuzzy TOPSIS approach was used to rank the IoT node alternatives. The results of the experiments indicate that the suggested framework improves the parameters under evaluation.  
However, the data storage optimization of blockchain nodes for IoT is not the subject of this article. Instead, we intended to investigate several optimization strategies to lessen the future framework's expanding storage load. Furthermore, in upcoming studies, the number of criteria may increase, increasing the number of node possibilities. This would make the model more robust and reliable. Moreover, GUI-based simulations could be carried out. Finally, using machine learning techniques for defect prediction of IoT nodes will be interesting in the future [51].  
Conceptualization, N.J.N. and A.H.; methodology, B.B.G.; software, B.B.G.; validation, N.J.N. and M.U.; formal analysis, B.B.G.; investigation, B.B.G., A.H., N.J.N. and M.U.; writing--review and editing, B.B.G., A.H., N.J.N. and M.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
All data are reported in the paper.  
Authors would like to thank editors and anonymous reviewers for their valuable input which has improved the quality of the manuscript substantially. Also, the authors thank [PERSON] for guiding them through the fuzzy approach.  
The authors declare no competing interest.