## 5 Experimental Results
As shown in Figure 8, the number of sensing rounds \(N_{RG}\) decreases with increasing value of the parameter \(\alpha\), which means the number of active nodes in a round \(M_{RG}\) increases with increasing value of \(\alpha\) and more sensing overlap may be introduced under this condition (Figure 11). Based on the above results, the parameters \(\alpha=0.5\) and \(\beta=1.5\) may be sensible settings for balancing the relationship between scheduling management and network performance. Therefore, depending on the requirement of the sensing task, these key parameters may be chosen to achieve desired performance. Figure 7: An example of generating a sensing round with the DASA approach. The third set of experiments explores the performance of the neural network model. The efficiency of neural network training can be improved with certain preprocessing steps performing on the network inputs and targets [34]. Figure 12 illustrates the preprocessing results of the network inputs and targets, which transform inputs and targets into a better form and then reverse transformed outputs back to the characteristics of the original target data. Figures 13 and 14 depict the learning and regression analysis of the network. Figure 13 shows that the network is learning since the mean squared error of the network is decreasing to a smaller value. The 6,514 input and target vectors are randomly divided into three sets. Four thousand vectors are used to train the network. Of these vectors 1,257 are used to validate how well the network generalized. Finally, the last 1,257 vectors provide an independent test of network generalization to data that the network has  
Figure 8: The number of sensing rounds \(N_{RG}\) versus \(\alpha\) with \(\beta=1\) (left) and \(N_{RG}\) versus \(\alpha\) with \(\beta=1.5\) (right). never seen. Moreover, regression analysis is employed as post-training analysis between the network response and the corresponding targets and three parameters are returned to evaluate the performance. The first two parameters, slope and y-intercept of the best linear regression relate targets to network outputs. If the outputs exactly equal to targets, the slope and y-intercept would be 1 and 0, respectively. For the 1-hop case, slope \(=0.79\) and y-intercept \(=-6.1\cdot 10^{-3}\). For the 2-hop case, slope \(=1.0\) and y-intercept \(=2.7\cdot 10^{-4}\). The third parameter is correlation coefficient between the outputs and targets. When the  
Figure 11: Average sensing overlap per round versus \(\alpha\) with \(\beta=1.0\) (left) and with  
correlation coefficient is equal to 1, then there is perfect correlation between targets and outputs. In this study, the correlation coefficients of 1-hop regression analysis and 2-hop regression analysis are \(RA=0.90\) and \(RA=1.00\) as shown in Figure 14 (left) and Figure 14 (right), respectively, which therefore illustrate a good fit. In order to simultaneously consider energy conservation, network connectivity, and the data gathering strategy, the fourth set of experiments investigates the impact of the transmission range \(R\) on the average number of round groups \(N_{RG}\) in the scheduling operation with \(l=300\) m. Figure 15 shows the relationship between the average number of round groups \(N_{RG}\) and the \(R/l\) ratio with varying the number of sensors. Figure 16 shows the comparison of the average number of round groups \(N_{RG}\) applying the  
Figure 12: The preprocessing results of the network inputs and targets. Figure 13: An independent test of network generalization. proposed scheduling schemes and those using the analytical models. To further explore the sensing load in a round, Figure 17 depicts the number of active nodes \(M_{RG}\) in a round with varying the transmission range \(R\). The result shows that the average number of group members in a round is between 1.5 and 2.5 for the proposed scheduling schemes. Observe that, as shown in Figure 15, the average number of sensing rounds \(N_{RG}\) increases as the ratio \(R/l\) increases (i.e.