# Cost-Aware IoT Extension of DISSECT-CF
## 4 Implementation and Validation
### Evaluation with Five Scenarios
Therefore, we defined a fixed case using 744 stations having seven sensors each, producing 100 bytes of sensor data per measurement, and a random case, in which we had the 744 stations with randomly sized sensor set (ranging between 6-8) and sensor data size (50, 100 or 200 bytes/sensor). The results can be seen in Figures 9 and 10 and Table 2. As we can see, we experienced minimal differences; the random case resulted in slightly more tasks. Furthermore, there are minimal differences between the cost of IoT providers, but we can see that even the small configuration differences can cause bigger variations of the costs, like in the case of Amazon. Table 2 shows how much virtual machines needed to process all generated data, and how many tasks were generated by the produced data in fixed and random cases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Amount of Data (Byte)** & **Number of VMs** & **Number of Tasks** & **Produced Data (GB)** \\ \hline
50 & 12 & 1153 & 0.261 \\ \hline
100 & 27 & 2299 & 0.522 \\ \hline
200 & 28 & 4486 & 1.044 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Used VMs, number of task and produced data in scenario N\({}^{0}1\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station Type** & **Number of VMs** & **Number of Tasks** & **Produced Data (GB)** \\ \hline fixed & 28 & 1555 & 0.348 \\ \hline random & 28 & 1561 & 0.352 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Used Virtual Machines (VMs), number of tasks and produced data in scenario N\({}^{0}2\). Figure 8: IoT and cloud costs in scenario N\({}^{0}1\). Figure 7: Number of virtual machines in scenario N\({}^{0}1\). In scenario N\({}^{\circ}\)3, we examined two sensor data generation frequencies. We set up 600 stations and defined cases for two static frequencies (1 and 5 min). In real life, the varying weather conditions were chosen to be \(\sim 10\) meters. The results are shown in Figure 9.  
Figure 10: Cost of the providers in scenario N\({}^{\circ}\)2. Figure 9: Results of scenario N\({}^{\circ}\)2.  
may call for (or result in) such changes. In both cases, the sensors generated our previously estimated 50 bytes. The results can be seen in Figures 11-13 and in Tables 3-5. The generated data in total: 0.321 GiB for 1 min frequency, 0.064 GiB for 5 min frequency. In this scenario, we used the three categories (small, medium and large) of the Azure cloud provider for both data generation frequencies. The first category contains 1 core and 1.75 GiB memory, the second contains two cores and 3.5 GiB memory, and finally we defined a virtual machine with eight cores and 14 GiB memory for the third category. The application needs more than 1400 tasks to process all generated data by the stations with 1 min frequency, while the data generated by stations with 5 min frequencies needed almost 300 tasks to process them. For this work, the application deployed 75 VMs with the small category by the Azure cloud provider in the 1 min case, and 10 VMs in the 5 min case. For medium category VMs, we needed 41 in the 1 min case, and five VMs in the 5 min case. Finally, 15 large category VMs needed to process in time for the 1 min case, but only one virtual machine was necessary to process all data generated in the 5 min case. As we can see from the results, the cheapest choice for operating the IoT side is Bluemix. To summarize, with this scenario, we have shown how small changes in the system parameters can affect the number of the virtual machines needed for sensor data processing, and the measurements also reflected how these parameters affect the final usage prices. Tables 3-5 show the detailed costs of the three test cases. The IoT costs are the same in all cases, but, from the cloud side costs, we can see that the stronger virtual machine we use, the more we should pay to operate the system. Figure 11: Results of scenario N\({}^{0}\)3. Figure 12: Number of virtual machines in scenario N\({}^{0}\)3.