# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 5 Results Analysis and Discussion
When we take a look at the time requirements of electromechanical and thermodynamic phenomena monitoring, it is clear that all prepared scenarios can handle this task. However, from the performance and economic point of view, it is preferable to choose Scenario 4 instead of Scenario 3 or 2 (since it requires fewer direct connections to the substation, which directly influence the total cost of WSN deployment and is characterized by a better capability for load balancing). Although we did not consider the scenario, where the data needs to traverse all sinks along the power transmission line in order to reach the base station as a promising WSN architecture for smart grids from the beginning, the above analysis confirmed this thesis even more. The issues raised by the first scenario can be easily noticed in Figure 8 (left graph), which contains time results for the left-subline (nodes of numbers from 1 to 22) and right sub-line (nodes from 23 to 45). The problem of workload imbalance can be observed for nodes, which reside closer (in terms of distance) to the substation (such as, for instance, node number 22 and 45). The time needed by them to serve network requests is equal to about 4.5 min (while the nodes which reside at the beginning of each sub-line need less than a minute to perform their task), and, what is more important, is not constant for each wireless node. Figure 8: Scenarios 1a,b, 2a,b: correlation between the sink number, time and power grid disturbance time frames (encryption/no encryption). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} _Scenario_ \\ \end{tabular} & \begin{tabular}{c} _Phenomena_ \\ \end{tabular} & \begin{tabular}{c} _Wave_ \\ \end{tabular} & \
\begin{tabular}{c} _Electromechanical_ \\ \end{tabular} &
\begin{tabular}{c} _Thermodynamical_ \\ \end{tabular} \\ \hline \(1(a)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \(1(b)\) & \(-\) & \(-\) & \(-\) & \(\checkmark\) \\ \hline \(2(a)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \(2(b)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \hline \(3(a)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \(3(b)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \hline \(4(a)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \(4(b)\) & \(-\) & \(-\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Possibility of the usage of each scenario for different power system dynamics. Scenarios, where wireless sensors are divided into groups, bring a significant improvement, as evidenced by Figures 8 and 9. Here, the workload is more or less stable and decreases for each sensor along with \(\eta\). Time delays are as well not so big as in the case of Scenario 1 and range between a couple of seconds (both for encrypted and unencrypted version). They, however, increase together with the number of hops inside the group. The most stable and delay-aware from the above 3, is the second scenario. At the same time, however, it is the most expensive one to deploy. On the other hand, the comparison of transmission times of encrypted and unencrypted data for Scenarios 5a,b can be observed in Figure 10. As it can be seen, the difference between transmission time is significant here. Gathered results clearly indicate, that when the encryption is used, the transmission time grows rapidly, causing greater time delays. On the other hand, when we consider the non-encrypted data transmission, it is evident that this approach performs better in terms of minimizing time delays. For the transmission line consisting of 45 electric poles, the longest transmission time is equal to, approximately 18 min. Analyzing the results gathered for different scenarios, it is worth examining how the distance between the poles affects the transmission time. As it can be observed from the results in Tables 11-15, when the distance between the poles changes, transmission time delay slightly increases. Consider, for instance, nodes 1, 22 and 45.