# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 5 Performance Evaluation
### Simulation Setup  
We consider the availability of five built-in sensors in smartphones: GPS, accelerometer, sound level, light sensor and camera. The power consumption of each sensor is presented in Table 2 based on the values in the literature [44; 45]. It is assumed that the sensing task time is following a Gaussian normal distribution between 1 and 5 min with a mean of 3 min. The time needed to prepare a smartphone is generated randomly between 1 and 5 s.  
According to [46], the power consumption of the preparation of the phone would be approximately 80 mW. With this in mind and by considering user profiles, we define the power overhead as described in Table 3 for each user profile. In more detail, as a highly-active user is active during 70% of the sensing task time, their smartphone needs 30% of the task time window to prepare for sensing, which translates to power overhead. Thus, for highly-active users, the power overhead consumption would be 0.30 \(\times\) 80 mW = 24 mW. The activity rates for the moderately- and least active users are 50% and 15%, respectively. Similarly, their power overhead would be calculated as 0.5 \(\times\) 80 mW = 40 mW for moderately-active users and 0.85 \(\times\) 80 mW = 68 mW for the least active ones. The power overhead of user \(i\) for task \(j\) contributes to the task energy consumption formulation in Equation (5) as \(P_{O_{j}}^{u_{i}}\). Sensing tasks are uniformly distributed over a 1000 m \(\times\) 1000 m terrain, and the users participate in sensing the tasks within a 30-unit range of their location, which is acquired by the GPS sensor in their smartphones. One thousand users are randomly labeled as highly-active participants, moderately-active participants and least active participants. The mobile devices have an initial residual energy that is calculated using historical data from real data gathered from TrackMaison. Highly-active users have 54% initial battery level, while this value for the moderate and least active users is 58% and 63%, respectively. The initial battery levels in this study are calculated by obtaining the average from the real traces collected by TrackMaison [41]. The arrival process of sensing tasks to the MCS platform is generated so as to follow a Poisson distribution with arrival rates varied between 20 tasks/min and 100 tasks/min. The duration of a sensing campaign is set to 30 min, while task durations are uniformly distributed between 1 min and 5 min. Task values are also uniformly distributed between one and seven. The results are averaged by running each simulation five times per each task arrival rate using five different seeds. The simulation settings are also summarized in Table 4.  
For simulating a user-centric mobile crowd-sensing model, we use a discrete-event simulator built in Java and run extensive simulations. In addition, we assume that participants do not move and leave the incorporation of mobility models into this study to future work. It is worth noting that using either model, as simple as a random waypoint mobility [47] or social mobility model, is possible [48] in the performance study of MCS systems. The users are uniformly distributed over the 1000 m \(\times\) 1000 m \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor** & **Power (W)** \\ \hline GPS & 0.6173 \\ Accelerometer & 0.00165 \\ Microphone & 0.223 \\ Light Sensor & 0.025 \\ Video camera & 1.258 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensor power consumption [44; 45]. \begin{table}
\begin{tabular}{c c} \hline \hline
**User Profile** & **Power Overhead (W)** \\ \hline Highly-Active User & 0.024 \\ Moderately-Active User & 0.04 \\ Least Active User & 0.068 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Power overhead of different profiles. terrain. The payment method in [39] is adopted in this study. Thus, each winner in the winner selection step is temporarily eliminated from the set of winners, and the winners' set is re-built.