# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 5 Empirical Evaluation
### Evaluation of the Participant Selection Model  
To evaluate the performance of our participants selection model presented in Section 3.6, we conducted extensive Matlab simulations, varying different parameters to study their impact on site quality score, site reliability score, site price, site probability of task  
Figure 19: Incident report panel for administrators. satisfaction, and site final selection score. The detailed evaluation results can be found in [16]. In this section, we provide a summary of some of the simulation results, namely, the impact of the event type, the percentage of malicious participants, and requested quality levels on the sites' final selection scores. Table 6 provides an overview of the simulation parameters used in the tests conducted. As shown in the table, the simulation considers realistic settings for crowdsensing, with an area size of 400 square meters, a total population varying from 100 to 200 individuals, with registered participants varying from 50 to 140 data collectors in the area of interest. Six types of events were considered in the simulation: (1) events related to traffic conditions are associated with GPS data; (2) traffic accident events require light, temperature, proximity, GPS, camera, and pulse data; (3) fire events require temperature, ambient temperature, camera, and pulse data; (4) storm events can be described by temperature, light, ambient temperature, relative humidity, camera, and pulse data; (5) a heart attack incident requires camera and pulse data; And (6) a volcanic eruption can be described by light, temperature, ambient temperature, proximity, relative humidity, GPS, and camera data. In this first test, the event type was varied from 1 to 6, while maintaining the other parameters (e.g., total population, participants, budget, required information quality level, country, and percentage of malicious participants) as constant. In the second test, the percentage of malicious participants was varied from 2 to 25% of the total number of participants. In the third test, the required information quality level was varied from 3 to 8. In the simulation, we considered 21 countries and used the statistical industry data in [16; 41] to simulate phone manufactures for the considered countries. We also gathered data pertaining to quality of the smartphone sensors. The data were gathered from the testing tools mentioned earlier (Dxomark, SensMark). The scores were normalized as a number out of 10. Next, we ran various tests to check if our algorithm behaves as designed. We also wanted to evaluate the effect of several data variables on sites' selection scores. The tests we ran concentrated on assessing the effect of the kind of event on the selection scores of locations. The kinds of events were simulated for the purpose of our test. Figure 20 shows the results for the selection of locations with respect to several kinds of requested events. In this simulated situation, the AoI was split into sixteen locations. There are potentially two malicious locations that have negative scores. The 10 th location (Site10) was chosen for several incidents including volcanic eruption and storm, whereas the 13 th location (Site13) was chosen for a traffic incident. Evidently, the kind of incident bore an effect on the locations' scores of selections.