## 5 Discussion
This complexity may pose limitations in real-time or resource-constrained IoT environments, requiring efficient resource management strategies. Additionally, the resolution of system parameters, including the fine-tuning of RIS elements and phase-shift configurations, is critical to achieving optimal performance but introduces further challenges in balancing accuracy and computational \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proposed Methods** & **RMSE** & **MAPE** & **SD** & **Mean Time (s)** \\ \hline
6 DChoA-DRL & 0.13 & 1.24\% & 0.27 & 486 \\ RL & 3.34 & 6.64\% & 5.21 & 549 \\ CNN & 6.91 & 8.17\% & 9.04 & 603 \\
6 DChoA & 5.27 & 7.96\% & 9.67 & 659 \\ DNN & 8.54 & 10.32\% & 11.43 & 724 \\ ChoA & 10.24 & 14.60\% & 17.29 & 851 \\ PSO & 12.76 & 17.29\% & 20.07 & 916 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of proposed methods for RIS phase-shift optimization. efficiency. Another significant challenge lies in obtaining accurate CSI, especially in dynamic IoT environments with varying interference and fading conditions. CSI estimation errors can degrade the system's overall performance, emphasizing the need for robust CSI acquisition and management techniques. These challenges highlight the trade-offs involved in implementing advanced optimization frameworks like 6 DChOA-DRL, which must be carefully managed to fully leverage their potential in practical IoT deployments. While the proposed DRL framework has demonstrated substantial advancements in optimizing the energy harvesting and communication performance for RIS-assisted IoT networks, several avenues for future research remain open. One critical direction involves developing algorithms with improved accuracy and reduced computational complexity to address the challenges posed by real-time applications and resource-constrained IoT environments. For instance, lightweight hybrid optimization frameworks that combine the exploration capabilities of metaheuristic methods with the rapid convergence of DL could offer significant improvements in scalability and efficiency. Another promising area is exploring the practical aspects of RIS-assisted networks, such as operating under unknown CSI in low-power IoT environments. Techniques for robust CSI acquisition and management, such as unsupervised learning or federated learning approaches, could help mitigate the challenges of unreliable or incomplete CSI, thereby enhancing system robustness. Furthermore, addressing RIS quantization errors and their impact on system performance is another critical research area. Methods that model and compensate for quantization imperfections in RIS hardware could improve energy efficiency and achievable rates while maintaining system reliability. Integrating advanced technologies, such as integrated sensing and communication (ISAC) and fluid antenna systems (FASs), into RIS-assisted IoT networks offers another exciting research avenue. ISAC could enable simultaneous sensing and communication, enhancing the functionality of IoT networks, while FASs, which dynamically adjust antenna configurations based on environmental conditions, could complement the RIS to further improve the signal quality and energy efficiency in IoT networks. Exploring the interplay between these technologies and RISs could yield groundbreaking solutions for future IoT applications, enabling more intelligent, efficient, and resilient networks.