# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 4 Simulations and Results
### Performance Evaluation of the Proposed Model Found on PSNR Comparison  
This section analyzes the result of sending images based on the proposed JOPSS MAC layer. For this purpose, at first, it is assumed that \(D_{m}=1s\) and S is equal to 5. Considering the arrival rates \(\lambda=[1,6,12,18,100]\) for the PUs and running this scenario, the results of Figure 12 are achieved. This figure compares the thermal camera output image (in RGB) in the transmitter and receiver as a successful transmission. As shown in the figure, the error energy for the color image is zero. This means that \(MSE=0\) and consequently the PSNR is infinite. Figure 11: Effectiveness evaluation of the proposed channel selection algorithm Minimum of Overhead Time Channel Selection (MOTS) on channels contribution. (**a**) The first scenario (increasing arrival rate from CNI = 1 to 10); (**b**) The second scenario (decreasing arrival rate from CNI = 1 to 10); (**c**) The third scenario (constant arrival rate for all sub-channels). Figure 12: A successful transmission within the allowed time. (**a**) The transmitted image; (**b**) The received image; (**c**) Error Energy (EE) image. Now, with a decrease of \(D_{m}\) from 1 s to 0.7 s, we deliberately provide conditions so that the overrun of the timer occurs. Figure 13 shows the comparison of transmitted and received images in an unsuccessful transmission. In this case, the whole image is not sent within the allowed time range, and about 36% of the image remains in the sender's buffer (whole of a sub color channel plus some pixels of other sub-channel). This has led to PSNR about 21.5 dB. In addition, Figure 14 compares the transmitted and received color images in an unsuccessful transmission. Due to the fact that all below the blue channel image was lost, in the image restored in the receiver, there is no information about the blue background of the original image, and the error energy image is also dominated by the blue color (Figure 14c). Finally, we plan to evaluate the effect of applying the proposed SAFE method on the improvement of the PSNR. We run again an unsuccessful transmission experiment with and without adding the SAFE algorithm. In Figure 15, the result of applying SAFE method can be observed. In this figure, the missing pixels which were 21% of the image are distributed well throughout the sub images. The error energy comparison in Figure 15b shows that lost pixels are well distributed throughout the image sub channels. Accordingly, the PSNR is improved to around 27.4 dB using SAFE. Figure 16 also shows the comparison of the reconstructed color image with its error energy in normal sending (without SAFE) and sending with the SAFE algorithm. Figure 13: Comparing the transmitted and received images in an unsuccessful transmission. (**a–c**) Red, Green, and Blue sub channels (Original image); (**d–f**) Red, Green, and Blue sub channels (Received image); (**g–i**) Error Energy (EE) images for sub channels. Figure 16: _Cont._  
Figure 14: Comparing the transmitted and received color images in an unsuccessful transmission;  
(**a**) Original image; (**b**) Received image; (**c**) Error Energy (EE) image. Figure 15: Results of applying the proposed JOPSS-SAFE method to improve PSNR; (**a**) Received image (sub channels); (**b**) Error Energy (EE) image (sub channels). Now, in Table 4, the comparison of PSNR is presented for the proposed method and other available techniques. It should be noted that in this table results were obtained using 1000 repetitions and finally the average PSNR were calculated for the sample image depicted in Figure 12a. The results clearly show that the proposed JOPSS model increased the image PSNR on average 3.2 dB while the JOPSS-SAFE model can even enhance it about 8.2 dB. In order to draw a wider evaluation of our proposed algorithm, Tables 5 and 6 are presented. In Table 5 some important IQA metrics such as SSIM, MSSIM, FSIM and PSNR are compared using our proposed JOPSS model.