## 5 Performance Evaluation
When the \(INR_{0}\) is set to 8, the proposed algorithm obtains the maximum SNR amplitude. The LLL method achieves the best performance only when \(INR_{0}=9\). In addition, when the \(INR_{0}\) is in the range 3-9, the proposed algorithm has a better SNR than the LLL method. Because the method of random node selection can hardly meet the requirement of \(INR_{0}\), the SNR performance for the situation without considering \(INR_{0}\) falls in the area between 11.5 dB and 12 dB. It can be seen in Figure 5 that the proposed algorithm can effectively improve the SNR performance under the given different \(INR_{0}\). The above analysis mainly focuses on a fixed number of social and physical sensor nodes. To further analyze the performance with respect to different number of social sensor nodes, we consider 4-12 social sensor nodes. Then, the SNR performance under \(INR_{0}=5\) dB is presented in Figure 6. The SNR performance of the proposed algorithm and LLL method decreases with the number of social sensor nodes. Specifically, when the number of social sensor nodes is greater than 9, the SNR amplitude reduces significantly for the proposed algorithm. Similarly, this situation also happens in LLL method. It shows that, although some social sensor nodes integrated into physical sensor nodes have positive role for the CB transmission in CPSS, excessive social sensor nodes have a negative impact on the transmission of CB. The reason is because the movements of excessive social sensor nodes can hardly meet the condition of \(INR_{0}\). When the \(INR_{0}\) is set as 7 dB, the impact on the transmission of CB is weakened. As shown in Figure 7, the SNR performance reduces slowly with the number of social sensor nodes. It is evident in Figures 6 and 7 that the number of social sensor nodes has an impact on the CB transmission. However, as the value of \(INR_{0}\) increases, the impact is weakened gradually. Figure 5: Average SNR performance for different \(INR_{0}\). Next, we analyze the influence of different values of \(\gamma\) and \(\rho\) for the dynamic mobility model. According to the model of dynamic mobility in [25], we consider two groups of model parameters: (a) \(\gamma=0.2\) and \(\rho=0.5\); and (b) \(\gamma=0.2\) and \(\rho=0.1\). The values of \(\gamma\) and \(\rho\) can determine the probability of preferential return. Based on the two groups of parameters, we can obtain different transition probabilities of social sensor nodes. The corresponding SNR performance for different mobility parameters is shown in Figure 8. It can be seen that the SNR performance when \(\rho=0.1\) is better than that of \(\rho=0.5\), for both the proposed algorithm and the LLL method. As the increase of the threshold \(INR_{0}\), the difference between the two groups of parameters reduces gradually. This is due to the following reasons. First, \(\rho=0.1\) means each social sensor node has greater probability to return to the previously visited nodes than that of \(\rho=0.5\). It reduces the difficulty of capturing the dynamic mobility for the proposed learning method. Second, as the value of the threshold \(INR_{0}\) increases, the constraint is weakened. The proposed algorithm can adapt to the change such that the difference of the SNR performance is reduced. Figure 6: SNR performance for different numbers of social sensor nodes (\(INR_{0}=5\) dB). Figure 7: SNR performance for different numbers of social sensor nodes (\(INR_{0}=7\) dB).