## 6 Conclusions  
This paper introduced a novel hybrid deep-learning framework, MOIBBO-CNN-LSTM, aimed at optimizing secrecy energy efficiency in CF-mMIMO-based IoT networks. By integrating CNN for spatial feature extraction and LSTM for temporal modeling, the proposed method effectively learns dynamic network behaviors and security-aware energy management strategies. To enhance optimization efficiency, the MOIBBO algorithm was employed for hyperparameter tuning, ensuring a balance between model complexity, convergence speed, and predictive accuracy. Extensive simulation results validated the superiority of the proposed framework compared to benchmark schemes, including NSGA-II-CNN-LSTM, ViT, DRL, CNN, and LSTM. Specifically, MOIBBO-CNN-LSTM exhibited the highest SEE, demonstrating faster convergence at earlier training epochs and outperforming NSGA-II-CNN-LSTM by achieving optimal SEE levels approximately 10% earlier. These results highlight the effectiveness of the MOIBBO optimizer in accelerating convergence while maintaining high predictive accuracy. Further analysis revealed that SEE improves with increasing AP transmit power up to a saturation point, beyond which excessive power consumption diminishes SEE gains. This underscores the importance of power control mechanisms to optimize energy efficiency while ensuring secure communication. Additionally, the study demonstrated that SEE decreases as the number of APs increases, implying that the secrecy rate gain from additional APs is counterbalanced by increased static power consumption in AP backhaul links. This observation suggests the necessity of adaptive AP selection strategies to deactivate idle APs during non-peak periods, an aspect that warrants further investigation. The findings of this study indicate that MOIBBO-CNN-LSTM provides a robust solution for energy-efficient and secure CF-mMIMO IoT communications. Future research will focus on adaptive real-time AP selection techniques and reinforcement learning-based optimization to further enhance network performance under varying traffic loads and security constraints.  
Conceptualization, A.V. and M.K.; methodology, A.V. and P.S.M.; software, M.S. and P.S.M.; validation, M.S., A.V. and P.S.M.; formal analysis, M.S.; investigation, A.V. and P.S.M.; writing--original draft preparation, A.V.; writing--review and editing, M.K.; visualization, M.K. and M.S.; supervision, M.K.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**RMSE**} \\ \cline{2-5}  & **Epoch = 50** & **Epoch = 100** & **Epoch = 200** & **Epoch = 300** \\ \hline MOIBBO-CNN–LSTM & 2.32 & 1.06 & 0.36 & 0.08 \\ NSGA-II-CNN–LSTM & 9.24 & 7.32 & 4.21 & 3.27 \\ ViT & 25.96 & 18.24 & 10.29 & 6.12 \\ DRL & 29.14 & 20.79 & 12.17 & 9.39 \\ CNN & 32.19 & 25.17 & 14.32 & 11.27 \\ LSTM & 35.27 & 24.80 & 15.20 & 13.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RMSE progression across different training epochs for the proposed methods.