# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 5 Empirical Evaluation
### Evaluation of the Participant Selection Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Type/Simulation** & **Impact of Event Type** & **Impact of \% of Malicious** & **Impact of Requested Quality** \\
**Parameters** & **on Site Selection Score** & **Participants on Site Selection Score** & **Level on Site Selection Score** \\ \hline
**Type of event(s)** & 1, 2, 3, 4, 5, 6 (variable) & 2 (traffic accident) & 3 (fire incident) \\ \hline
**Total population** & 200 & 100 & 100 \\ \hline
**Participants** & 140 & 50 & 50 \\ \hline
**Budget** & 1000 & 100 & 100 \\ \hline
**Area size** & 20 \(\times\) 20 = 400 m\({}^{2}\) & 20 \(\times\) 20 & 20 \(\times\) 20 \\ \hline
**Time window for task** & 1 min & 1 & 5 \\ \hline
**Required information** & 5 & 4.5 & 3, 4, 5, 8 (variable) \\
**quality level** & & & \\ \hline
**\% of malicious participants** & 2 & 2, 5, 10, 25 (variable) & 2 \\ \hline
**Country** & 16 Malaysia & 8 Poland & 8 Poland \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tests’ configuration parameters. We report interesting findings. For example, incidents related to traffic led to scores in the scope of 900, whereas events related to fire led to scores in the scope of 400. Furthermore, incidents related to storms led to scores in the range of 300 (the lowest) as it needed 6 sensors (some of which are specialized). As such, the results show that incidents needing specialized or a higher number of sensors lead to lower odds of being fulfilled. Furthermore, locations with more advanced smartphones have better opportunities of being chosen for incidents. We ran other tests that concentrated on assessing the effect of participants' malpractice on the scores of the locations' selection. In these tests, we created various percentages of participants misusing the system. Figure 21 depicts the selection of the choices regarding several rates of participants misusing the system in the area of interest (AoI). In this situation, AoI was split into four locations. The first location (Site 1) was chosen for the situations where one-tenth and one-fourth of the participants misused the system, while the third location (Site 3) was chosen for the situations with two and five percent of the participants misusing the system. As depicted in the figure, as the rate of misusing the system rose, more locations had participants misusing the system and were removed. For example, with two percent misusing the system, one out of four locations had misusing participants. Furthermore, with five percent, two out of four locations had participants misusing the system, whereas for ten percent and twenty five percent, three of four locations had users who misused the system. This shows that the level of misusing the system effects the chance of fulfilling the requests. In the following tests, we concentrated on assessing the effect of the needed level of quality on the locations' selection scores. As such, we created various levels of needed quality. Figure 22 depicts the results for the location selection concerning the needed levels of quality. The AoI was split into nine locations. The sixth location (Site 6) was chosen when Levels 3 and 4 of quality were needed. On the other hand, the seventh location (Site 7) was chosen when Level 5 quality was needed. However, none of the locations were chosen when Level 8 quality was needed. This test shows that requesting higher quality results in a lower opportunity of finding a selection since the overall score of quality should be the same or more than the needed level of quality. Figure 20: Sites’ selection scores across varying event types. It is important to mention that the proposed selection approach revolves around the selection of the smallest and most reliable group of participants that can provide the best quality possible for the required sensory data. This helps ensure scalability and minimal latency resulting from the participants' selection and data collection processes.