## 5 Performance Evaluation
### Compression Efficiency  
Experiment I looks into the effect of node number on video compression efficiency. We deploy various numbers of video sensors in a field. The sink records their FoV parameters. The video sensors' sensing radius is set to be 30 m, and the offset angle is set to be 60\({}^{\circ}\). The locations and sensing directions  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Number of video sensors & 200 \\ Number of channels & 10 \\ Transmission range & 15 m \\ Offset angle & 60\({}^{\circ}\) \\ Sensing radius & 30 m \\ Image size & 256 \(\times\) 256 \\ \(E_{\mathit{elce}}\) & 50 nJ/b \\ \(\epsilon_{amp}\) & 10 pJ/b/m\({}^{2}\) \\ \(\alpha\) & 2 \\ Transmission rate & 2 Mbps \\ \(CT\) & 1 \\ \(DT\) & 0.8 \\ \(I_{0}\) & 1.196 mA \\ \(N_{\mathit{cyc}}\)(encoder) & 2.3 Mcycles \\ \(C_{total}\) & 0.67 nF \\ \(N_{\mathit{cyc}}\)(decoder) & 0.14 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
of each video sensor are fixed. Each sensor captures one image at each deployment, and the size of each image is \(256\times 256\). Each image is segmented into blocks of \(8\times 8\) before performing DCS. We first encode original images by DCS. Then, we reconstruct the images received at the sink. We process the image through matrix operations in MATLAB. We obtain images' reconstruction error rate using (30). Figure 5 shows the results of DCS compression efficiency under the different number of nodes. We give the reconstruction error rate and PSNR achieved by CDCS and DCSR.  
When an image \(X\) has a sparse representation in one basis, it can be reconstructed by a small number of projections onto a second basis that is incoherent with the first. The reconstruction error rate of \(X\) is calculated by \(1-P_{D}\left(X\right)\). From Figure 4(a), the reconstruction error rate of image declines with the increase of node number. When the node number in the network is high, the reconstruction error tends to zero, and the image shows a good reconstruction effect. Without considering correlation, nodes are clustered randomly, and some nodes that have not observed the objects may appear in the cluster. Because there is no object in the observation of these nodes, the compression for the object cannot be realized. As a result, the number of nodes participating in DCS decreases and the reconstruction error rate increases. The increase in the number of chosen nodes increases the probability of the node to observe the object, making up for the disadvantage without consideration of the correlation to a certain extent. Meanwhile, there exists Gaussian noise while nodes acquire images. When the number of nodes increases, the intensity of the signal increases gradually, resulting in a larger PSNR.  
We present reconstructed images shown in Figure 6 to more intuitively compare the effects of image reconstruction. This result corresponds to the PSNR values in Figure 4(b) when the number of nodes is 20. We can see that CDCS achieves a better reconstruction effect than DCSR. By carefully comparing the front and rear wheels of the vehicle, it can be seen that the image distortion of the CDCS is lower than that of DCSR. One main reason is that CDCS can drive enough nodes to participate in compression.  
Figure 5: Compression efficiency.