# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 5 Results Analysis and Discussion
While such a network can provide extremely low latency data transmission, this model is highly cost inefficient as it incurs huge installation and subscription costs. This is why this solution can not be used in real-life deployments. Because the problem of finding optimal locations of cellular transceivers is such a complex task, that two previous scenarios failed to succeed, we proposed to consider another approach (Scenarios 3a,b, 4a,b). Since the idea of splitting relay nodes into groups succeeded in terms of workload balancing, we decided to adhere to established rules. However, we modified them such that we divided relay nodes into less than 45 groups, in order to keep the balance and additionally decrease cost. As we take a look at the results in Tables 6 and 7, we see that the performance of each sink is still stable, even with the smaller number of groups (\(\kappa=12\) for Scenario 3, and \(\kappa=8\) for Scenario 4). Also here, the time increases along with the number of hops, but the growth is not so rapid as in the case of Scenario 1. Moreover, the difference between secure and insecure version remained more or less constant. \begin{table}
\begin{tabular}{c c c|c c c|c c c|c c c|c c} \hline
**Sink** & **Time\({}_{\text{NE}}\)** & **Time\({}_{\text{E}}\)** & **Sink** & **Time\({}_{\text{NE}}\)** & **Time\({}_{\text{E}}\)** & **Sink** & **Time\({}_{\text{NE}}\)** & **Time\({}_{\text{E}}\)** & **Sink** & **Time\({}_{\text{NE}}\)** & **Time\({}_{\text{E}}\)** \\ \hline
**1** & 8.72 & 9.97 & **11** & 14.6 & 74.5 & **21** & 29.57 & 248.91 & **31** & 8.36 & 39.4 & **41** & 21.29 & 180.85 \\
**2** & 8.9 & 11.48 & **12** & 15.69 & 87.0 & **22** & 31.58 & 272.4 & **32** & 9.25 & 48.6 & **42** & 23.09 & 201.04 \\
**3** & 9.16 & 14.08 & **13** & 16.86 & 100.61 & **23** & 4.61 & 5.41 & **33** & 10.23 & 58.91 & **43** & 24.97 & 222.33 \\
**4** & 9.5 & 17.78 & **14** & 18.12 & 115.29 & **24** & 4.75 & 5.93 & **34** & 11.3 & 70.31 & **44** & 26.95 & 244.72 \\
**5** & 9.95 & 22.58 & **15** & 19.48 & 31.08 & **25** & 4.98 & 7.3 & 35 & 12.46 & 82.81 & **45** & 29.02 & 268.21 \\
**6** & 10.5 & 28.49 & **16** & 20.93 & 147.97 & **26** & 5.32 & 9.9 & **36** & 13.72 & 96.42 & **46** & \\
**7** & 11.13 & 35.49 & **17** & 22.48 & 165.95 & **27** & 5.74 & 13.6 & **37** & 15.05 & 111.1 & **47** & & & \\
**8** & 11.86 & 43.59 & **18** & 24.11 & 185.04 & **28** & 6.26 & 18.4 & **38** & 16.47 & 126.89 & **48** & & & \\
**9** & 12.68 & 52.79 & **19** & 25.84 & 205.23 & **29** & 6.87 & 24.3 & **39** & 17.99 & 143.78 & **49** & & & \\
**10** & 13.6 & 63.09 & **20** & 27.66 & 226.52 & **30** & _7.57_ & 31.3 & **40** & 19.59 & 161.77 & **50** & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Scenario 1: transmission times (in s) for each sink all over the transmission line. (Distance between poles is equal to 100 m, \(\kappa=2\), \(\eta=22\), \(\eta=23\), NE means no encryption, E means encryption.) \begin{table}
\begin{tabular}{c c c|c c c c c c|c c c|c c c} \hline
**Sink** & **Time\({}_{\text{NE}}\)** & **Time\({}_{\  
We made a correlation between the number of relay nodes on the way to substation, the time taken by sinks to deliver measured data together with power grid disturbance time frames (Figures 8-10). Additionally, in Table 9 we gathered existing power systems transients and using simulation results, determined which from prepared scenarios can be used for monitoring specific transmission grid phenomena. It turned out, that different scenarios are capable of monitoring different phenomena, and in order to achieve the most reliable, useful results and react in a timely fashion to power grid disturbances, a profound analysis of the transmission grid architecture should be accomplished. As it is evident from Table 9, wireless sensors seem to be not the best choice when it comes to wave and electromagnetic phenomena monitoring. (This applies to Scenarios 1, 2, 3 and 4. For this type of power grid disturbances, one needs a faster solution (for instance, the optical fiber).