## 8 Conclusions  
Cognitive sensors have limited resources, and if no appropriate approach is used during deployment, the sensors are disconnected due to redundant sensing of the same data. The collection of redundant information in IoT networks has an impact on system performance, including energy efficiency, reconfiguration time, latency, and packet loss. To address these issues, we suggest a new reinforcement learning-based technique, \"Adaptive scheduling in cognitive IoT sensors to optimize network performance using reinforcement learning (ASC-RL)\". It constructs a function for a state-changing policy from three kinds of parameters: internal parameters (states), environmental parameters (sensing values), and historical parameters (energy levels, roles, and number of switching states). These states eliminate extensive sensing, reduce processing costs, and minimize communication over the radio link. The suggested system minimizes network traffic while improving network performance in terms of energy. The primary parameters assessed are joint Gaussian distributions and event correlations, with the resulting signal intensities, noise, prediction  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **CAS-IIoT-RL** & **LSTM-RL** & **AEM-RL** & **ASC-RL** \\ \hline Algorithm & DQN & LSTM & A2C & PPO \\ Space & Feature \(V_{c}\) & Time-Series & QoS Metrics & \(S_{sn},\mu\) \\ \(A_{cn}\) & Discrete & Discrete & Continuous & Discrete \\ \(\tau\) & 0.001 & 0.0005 & 0.0003 & 0.0003 \\ \(\lambda\) & 0.9 & 0.95 & 0.98 & 0.99 \\ Archetteure & 2-layer NN & LSTM + Dense & 3-layer NN & 3-layer NN + ReLU \\ Episodes & 5000 & 7000 & 8000 & 10,000 \\ Reward \(R_{t}\) & Delay, Energy & Latency, PDR & Latency, Energy & Energy, Delay \\ Environment & Static IIoT & Edge IIoT & Edge+ Fog & Dynamic IoT \\ Implementation & Python-2 & TensorFlow 1.x & Python + Keras & PyTorch + Gym \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of ASC-RL and CAS-IIoT-RL, LSTM-RL, and AEM-RL.  
Figure 13: Comparative Performance of ASC-RL with CAS-IIoT-RL, LSTM-RL, and AEM-RL (Success Rate).  
accuracy, and energy efficiency with a combined reward score. In a comparative analysis, ASC-RL improves the overall system performance by 3.5% in detection and transition probability. The probability of false alarms is reduced by 25.7%, the transmission success rate increased by 6.25%, and the energy efficiency and reliability thresholds are increased by 35%.  
In future work, we intend to create a Q-learning approach combined with reinforcement learning and apply the transfer learning procedures that allow rewards and agents to apply generic knowledge in the same tasks, reducing time and functional cost. To obtain high learning performance, we intend to incorporate prioritized sampling, experience replay, and adaptive exploration.  
M.N.K.: conceptualization, methodology, software, validation, and writing--review and editing. M.S.: supervision and writing--review and editing. S.L.: supervision and reviewing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety (koFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (RS-2021-KN051410, Development of cyber incident response assessment technology for critical digital assets of nuclear power plant (NPP))  
The authors declare that none of the work described in this publication may have been influenced by any known conflicting financial interests or personal ties.