# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 5 Empirical Evaluation
### Evaluation of the Participant Selection Model
To illustrate the performance and scalability of the algorithm in different scenarios, four incidents are depicted in Figure 23. Figure 23a pertains to a traffic condition monitoring scenario in Sweden. In this scenario, the configuration parameters were as follows: Country = Sweden; Event = Traffic condition monitoring (Event 1); Task time = 10 min; # of participants = 35 participants out of 60 users' population. The area of interest was divided into 9 sites in that case, and Site 8 was selected for the task, with the lowest price score among the 9 sites (i.e., a score of 3.97), as well as 6 participants, and a site kappa score of 1.0267. The selected site in highlighted with a green square in Figure 23a. Figure 23b depicts a fire incident scenario in Germany. In this scenario, the area of interest was divided into 25 sites, 19 of which contained participants. The site selected in this scenario was Site 11 containing 2 participants (highlighted in green), which had an average sensor quality score of 7.76 (the highest sensor quality score out of all sites), a proximity of 0 from the event of interest (i.e., centered at the event of interest), and an average site's residual battery level of 75%. The total site's quality score obtained was 0.655238--representing the lowest value and thus the highest site quality levels. Figure 21: Sites’ selection scores for varying percentages of participants misusing the system. Figure 22: Sites’ selection scores for varying needed levels of quality. Figure 23c illustrates a traffic accident scenario in India. The area in this scenario encompassed 19 sites with participants and 6 empty sites. Site 18 containing 2 participants was selected in that case, with a site quality score of 1.0568, a residual battery level of 47%, a proximity level of 22.36 m from the event of interest, and a sensor quality score of 4.749. In comparison to the first scenario occurring in Germany, in which the most popular phone brands are the Samsung Galaxy S7, Samsung Galaxy A5, iPhones 6, 6S, and 7--i.e., brands with high variety and high-quality sensors, the second scenario took place in India, with phones containing fewer sensors with lower sensor quality scores (e.g., Lyf Jio F90M, Samsung Galaxy J2, Xiaomi Redmi Note 4, Lyf LF-2403N, Samsung Galaxy J7 Prime). This resulted in 18 out of 19 sites being eliminated from the selection due to the inability to meet the minimum required information quality level--i.e., 4.5 in that case. The only site that was able to meet that requirement was Site 18, with a sensor quality score of 4.749. In comparison to the first scenario occurring in Germany, in which the most popular phone brands are the Samsung Galaxy S7, Samsung Galaxy A5, iPhones 6, 6S, and 7--i.  
of 4.749, and thus it was selected despite its low residual battery level and poor overall quality score. Figure 23d depicts a storm incident in Malaysia. In that scenario, Site 8 containing four participants was selected with a close proximity from the event of interest (10 m), a high residual battery level (of 73.5%), and a sensor quality score exceeding the required level (4.865). Due to the mixed nature of popular phones in Malaysia, 11 out of 18 sites were not selected due to lack of ability to meet information quality requirements. Evaluation of the Models for Estimating Data Quality, Participant Reputation, and Filtering Outliers  
As mentioned, since we cannot neglect the possibility of malicious or faulty data being sent either intentionally or carelessly by the selected participants, we must determine the quality of data received at the server. For the evaluation of our model presented in Section 3.7, we simulate a crowdsensing scenario to measure the temperature in an AoI. We used Python libraries such as pandas and numpy, typically used for scientific computing, data analysis, and the seaborn library based on matplotlib for data visualization.