# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 5 Conclusions  
In this paper, due to the importance of intelligent monitoring, inspection and maintenance technologies in emerging smart grids, a special architecture for image streaming in wind turbine farms was presented based on cognitive radio networks (CRNs). We presented the designing of a customized cognitive radio MAC layer for bulky and delay-sensitive data transmission. In the proposed model, the idea of minimizing time overhead due to both retransmission and SS time, without reducing SS time, was presented well. The distinctive feature of our model is that unlike similar methods in the literature, we did not insist on reducing spectral sensing time. The reason is that although it seems any reduction in SS time can reduce the time overhead caused, actually it will increase the false alarms and also miss detection probability. This means lower channel efficiency and quality of service (QoS) are not avoidable. Therefore, we targeted joint optimization of number of spectrum sensing iterations (\(NSSI\)) and the packet size, as two main optimization parameters, using our JOPSS model for delay-sensitive communications. To this end, we defined our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT) along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). Furthermore, in order to increase the system efficiency, we did not count only on the time overhead reduction. We also proposed the ranking procedure, MOTS, for the optimal selection of sub-channels during the image transmission. It forces the transmitter to select only sub-channels that impose the least time overhead during the transmission. Besides, in order to enhance the PSNR while missed pixels were accumulated at the end of the image, we presented our SAFE method based on the scrambling of the image in the transmitter and reconstructing it with applying smoothing filter in the receiver. It should also be noted that we conducted our implementation according to restrictions defined for bulk and delay-sensitive data transmission over smart grids in IEEE 1646 [29] and IEC 61850 [28] standards. The amount of PSNR achieved found on proposed methods JOPSS and JOPSS-SAFE are 29.67 dB and 31.79 dB, respectively. The achieved results indicate that the proposed algorithms outperform strongly other existing approaches based on both PSNR and FSIM metrics.  
Today, Deep learning (DL) techniques get more attentions in computer network applications due to their excellent achieved results. In particular in resource allocation problems, DL has shown a tremendous impact in recent years. Therefore, we are going to replace the exhaustive search method used in our study with DL to reduce time cost of parameter estimation step in the optimization problem. Besides, due to this fact that different IQA metrics perform differently for various distortion types so fusion of these metrics would be a useful and practical suggestion found on machine learning and DL methods for future works.  
Conceptualization, M.B., S.A.M., and Q.X.; methodology and software, M.B., and S.A.M.; validation, M.B., S.A.M., and Q.X.; formal analysis, M.B., S.A.M., and Q.X.; investigation, M.B., S.A.M. and Q.X.; writing--original draft preparation, M.B.; writing--review and editing, M.B., S.A.M., and Q.X.; visualization, M.B., S.A.M., and Q.X.; supervision, S.A.M.; project administration, M.B.; funding acquisition, Q.X.  
This research received no external funding.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**D** & [72] & [52] & [96] & **JOPSS** \\ \hline
0.45 S & 0.92 & 0.91 & 0.83 & 0.94 \\ \hline
1.00 S & 0.94 & 0.94 & 0.83 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison based on FSIM.