# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 4 Simulation and Analysis
### Simulation Result Analysis
Traditional algorithms like DQN and MD3 QN exhibit larger standard deviations (e.g., video: DQN = 0.45 and MD3 QN = 0.35), reflecting sensitivity to local channel load variations. While federated learning algorithms (FL-DQN and FL-DDQN) improve distributed training, their unoptimized model structures and communication mechanisms still result in higher latency deviations (FL-DQN: 0.30; FL-DDQN: 0.25). FL-MD3 QN's service-adaptive multi-objective reward function ensures minimal standard deviations across all service types, validating its stability in dynamic multi-objective optimization. This underscores its superior adaptability and precision in balancing spectrum efficiency, latency, and reliability under complex IoT access conditions. As shown in Figure 4, during initial iterations, all models exhibit low average access success rates. As iterations increase, the FL-MD3 QN algorithm rapidly improves its success rate and reaches a high level within fewer steps. In contrast, DQN and MD3 QN show slower growth in success rates and lower final convergence values. This highlights FL-MD3 QN's faster convergence speed and higher precision. After introducing FL, FL-DQN and FL-DDQN achieve improved success rates but still underperform compared to FL-MD3 QN. By integrating MD3 QN's strengths with FL, FL-MD3 QN enables collaborative optimization across multiple construction site IoT systems. Its efficient learning mechanisms further enhance data transmission access rates. The rapid convergence of FL-MD3 QN's success rate demonstrates its ability to dynamically adjust bandwidth allocation of APs within sites. This avoids transmission failures caused by channel competition, improving overall construction site management efficiency. The proposed framework achieves rapid convergence, indicating that edge servers enhance model convergence speed. As shown in Figure 5, initial average reward values across models are similar. However, as iterations increase, the FL-MD3 QN algorithm rapidly improves its average reward and reaches a high level quickly. Specifically, by the 1000 th iteration, FL-MD3 QN achieves an average reward of 5.23, significantly outperforming DQN (2.87), MD3 QN (4.55), FL-DQN (4.20), and FL-DDQN (4.31). The reward growth rate of FL-MD3 QN is the fastest, reflecting its superior learning and optimization  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Delay (s)**} \\ \cline{2-5}  & **Text** & **Audio** & **Image** & **Video** \\ \hline DQN & 0.80 (\(\pm\)0.12) & 0.90 (\(\pm\)0.15) & 1.30 (\(\pm\)0.20) & 3.40 (\(\pm\)0.45) \\ MD3 QN & 0.60 (\(\pm\)0.10) & 0.75 (\(\pm\)0.12) & 1.00 (\(\pm\)0.18) & 2.80 (\(\pm\)0.35) \\ FL-DQN & 0.4  
capabilities. In contrast, DQN shows slow reward growth, indicating limited adaptability in complex transmission environments. MD3 QN improves growth speed but remains inferior to FL-MD3 QN. FL-DQN and FL-DDQN, while incorporating FL, still have room for performance improvement. FL-MD3 QN achieves high average rewards with fewer iterations and maintains stable growth. This demonstrates robust convergence and stability, enabling the algorithm to identify optimal access strategies efficiently. Such capability enhances data transmission efficiency and reliability in dynamic scenarios. To validate the effectiveness of the reward function design, Table 7 compares four schemes: Scheme 1 is the proposed service-adaptive multi-objective reward function. Scheme 2 is fixed-weight multi-objective reward (a = 0.3, b = 0.3, c = 0.4). Scheme 3 is  
Figure 4: Average access success rate. Figure 5: Average reward value. single-objective reward (maximizing transmission rate only). Scheme 4 is unconstrained reward (ignoring BER). Experimental results demonstrate the superiority of Scheme 1: multi-objective optimization. Compared to Scheme 2, Scheme 1 achieves a 26.9% increase in average reward value, a 28.6% reduction in video latency, and a 62.5% improvement in BER.