# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 5 Performance Evaluation
### Simulation Results
The results regarding the platform and user utility under these arrival rates comply with these as the algorithm successfully addresses the trade-off between the platform and user utilities by recruiting slightly smaller number of participants and at the same time maximizing the platform and user utilities. Figure 14 illustrates the number of dead phones after the MCS event, which is an undesirable situation for the participants. in SOBER-MCS, the platform prevents from assigning sensing tasks to mobile devices that are very likely to decline accepting the task assignment due to the low battery level since by accepting the assignment, the phone may turn off before the completion of sensing. As seen in  
Figure 12: Average energy consumption. Figure 13: User participation in different scenarios: (**a**) mobile crowd-sensing benchmark; (**b**) SOBER-MCS with \(\Re=10\%\); (**c**) SOBER-MCS with \(\Re=15\%\); and (**d**) SOBER-MCS with \(\Re=20\%\). the figure, SOBER-MCS achieves a reduction in the number of dead phones such that it is a negligible value. Just for the case when the minimum residual energy is set to 10%, a small number of dead phones is experienced. However, by comparing it to the MCS benchmark, the number of dead phones even under this setting is negligible. Under all arrival rates, the results show a sharp decrease in the number of dead phones, which will further incentivize the participants to keep collaborating with the MCS platform. Figure 15 presents the number of breakout mobile devices. A breakout device is a mobile phone with a battery level below the minimum residual energy. This figure actually reveals the success of SOBER-MCS in balancing the total energy consumption of the platform by excluding the users with a lower than the allowed minimum residual energy level. In this paper, the following values are considered for the minimum residual energy: 10%, 15% and 20%. As seen in the figure, the number of breakout phones grows remarkably by increasing the task arrival rate from 20 task/min to 100 task/min. When the arrival rate is 20 task/min, the proposed algorithm with the minimum residual energy of 10% doubles the number of breakout phones that are experienced under the benchmark approach. When the minimum residual energy is set to 15% and 20%, the number of breakout phones further increases, as expected. The number of breakout phones increases as the arrival rate becomes heavier since a heavier load of sensing tasks is expected to drain more energy from a smartphone battery due to the higher utilization of the built-in sensors. It is worth mentioning that the number of dead-phones is excluded from the presentation of the breakout phones. Furthermore, the number of breakout phones denotes a cumulative value such that the number of breakout phones for each auction contributes to the total value that is presented in the figure. For instance, if user \(i\) fails to sense the task in auction \(\tau\) and in the next auction \(\tau+1\), he/she re-tries to sense the task, and he/she will be discarded one more time by the platform; thus, two breakouts will be recorded by the MCS platform for user \(i\). Figure 14: Number of dead phones: (**a**) mobile crowd-sensing benchmark; (**b**) SOBER-MCS with \(\Re=10\%\); (**c**) SOBER-MCS with \(\Re=15\%\); and (**d**) SOBER-MCS with \(\Re=20\%\).