# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 3 Results and Discussions
### Evaluation of Network Coverage Optimization Model  
To ensure the reliability of the network coverage optimization scheme, it is essential to minimize packet loss caused by data transmission over low-quality links. To determine the optimal number of sensor nodes, network coverage optimization simulations were conducted with 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, and 40 sensor nodes, respectively. The evaluation metrics CR and OR were employed to comprehensively assess the deployment of sensor nodes in the monitoring area, as illustrated in Figure 5. To mitigate the effect of randomness, the PSO algorithm was repeated for 50 trials at each number of sensor nodes and the averaged value was accessed. Figure 4: Curve fitting between path loss and distance. As the number of sensor nodes increased sequentially from 25 to 40, the CR increased from 85.68% to 97.83% and the OR increased from 11.29% to 57.31% (Figure 5a,b). As  
Figure 5: Correlation between coverage rate (CR), overlap rate (OR) of the wireless sensor network, and number of sensor nodes. (**a**) Average value of CR with different number of sensor nodes; (**b**) average value of OR with different number of sensor nodes; (**c**) curve slope of CR and OR. illustrated in Figure 5c, when the number of sensor nodes within the monitored area was less than 33, the curve slope of CR was relatively steep, ranging from 0.846 to 1.778. As the number of sensor nodes exceeded 33, the curve slope of CR decreased significantly, falling within the range of 0.002 to 0.664. Conversely, when the number of sensor nodes was less than 33, the curve slope of OR was relatively small, ranging from 2.101 to 3.032. Once the number of sensor nodes surpassed 33, the curve slope of OR increased markedly, ranging from 3.602 to 3.881. It could be concluded that when there were less than 33 sensor nodes, the increase in the number of nodes had a significant effect on the increase in CR and a relatively small effect on the increase in OR, while when there were more than 33 sensor nodes, the increase in the number of nodes had a significantly lower effect on the increase in CR and a significantly higher effect on the increase in OR. In order to monitor the largest possible area using the smallest number of nodes, the optimal number of sensor nodes in a 45 m \(\times\) 45 m area was recommended as 33 in this study. In order to obtain the optimal deployment location of 33 sensor nodes, 30 rounds of network coverage optimization experiments were conducted by applying the particle swarm optimization algorithm. Each round was iterated 500 times, and the obtained CR and OR are shown in Figure 6. The value of CR was between 92.296% and 97.512%. The optimal deployment of the 33 sensor node locations with the largest CR (97.512%) and the smallest OR (30.479%) was selected and illustrated in Figure 7.  
Figure 6: Scatterplot of network coverage and overlap. CR represents the coverage rate of the wireless sensor network, and OR denotes the overlap rate of the wireless sensor network. The results, as shown in Figure 6, indicate that the PSO algorithm effectively enhanced network coverage by iteratively optimizing sensor node positions. The high CR (maximum of 97.512%) suggests a robust deployment configuration with near-complete coverage of the monitored area, which is critical for applications requiring comprehensive data acquisition. The relatively low OR (minimum of 30.479%) further highlights the efficiency of the selected configuration in minimizing redundant sensing areas, thereby reducing energy consumption and potential data overlap. The observed variation in CR values across the 30 experiment rounds reflects the stochastic nature of the PSO algorithm, of which performance depends on the initial distribution of particles and the exploration-exploitation balance achieved during optimization. This variability underscores the importance of multiple trials to identify the best configuration for ensuring reliable and optimal deployment.