# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 5 Performance Evaluation
### Simulation Results
The platform-user utility trade-off is addressed through the breakout phones that participate in the sensing campaign with alarming battery levels. Exclusion of the breakout phones allows for the remaining participants to be assigned more tasks and consequently be rewarded higher than a scenario where all participants are considered in the auction regardless of their residual battery levels. Figure 11 illustrates the average remaining battery levels of mobile devices at the end of each event. In this study, Nexus 7 tablets with 16 WH battery capacity are used. Therefore, 100% battery level in the figures translates to 16 Wh battery power. First of all, as one can tell intuitively, heavier sensing task arrivals will drain more battery power as the recruitment schemes in the figure behave under varying sensing task arrival rates. Energy-unaware recruitment of users may end up having some of the participants' smartphones left with fully-drained battery power. Therefore, discarding those with insufficient battery levels upfront will improve the average residual battery level at the end of the sensing campaigns. Furthermore in SOBER-MCS, users considering the energy overhead in relation to their sociability factors results in the platform being inclined toward recruiting those with higher sociability in order to avoid compensating the high energy overheads of inactive users. As expected, the highest battery drain is under heavily-arriving sensing task requests (i.e., 100 tasks/min). As can be clearly seen in the figure, SOBER-MCS improves the residual battery level as the sensing task arrival rate increases from 20 task/min-100 task/min. Under the sensing task arrival rate of 20 task/min, SOBER-MCS saves the battery of mobile devices by roughly 5% when compared to the benchmark when the residual energy threshold is set to 15% of the battery level, whereas the improvement is 3.7% when the residual energy threshold is set to 20%. Under a heavy task arrival rate (i.e., 100 tasks per min), SOBER-MCS improves the residual battery level performance of the benchmark scheme by 3%, 7% and 13% when the residual energy threshold is set to 10%, 15% and 20% of a fully-charged smartphone battery, respectively. Overall, SOBER-MCS is able to reduce the energy costs of participants in all scenarios, which is expected to result in user satisfaction and eventually incentives for more users to participate in MCS events. Figure 12 is a complement to Figure 11. As the residual energy of mobile devices increases, it is intuitive to expect the average energy consumption to decrease. As seen in the figure, the average energy consumption of SOBER-MCS under all battery level thresholds for all task arrival rates is lower than that under the benchmark scheme. Figure 11: Average residual energy under SOBER-MCS with various residual battery levels. Figure 13 depicts the recruitment policy of SOBER-MCS in comparison with the benchmark scheme in terms of the number of participants of different user profiles on average. As seen in the figure, the total average number of participants in all policies and under different arrival rates coincide, which means that SOBER-MCS does not impose additional cost to the platform by recruiting more users, and this behavior is independent of the value of the residual battery threshold. Instead, the algorithm attempts to make a compromise between the platform and user utilities by selecting users at the right time and by considering their availability. Thus, the user with higher probability to accept the task based on their availability undertakes the task. When a user is active (i.e., in interaction with the smartphone) at the time of sensing, the built-in sensors of the smartphone are enabled and are ready to accept the task. In this case, the user does not experience the energy overhead to start the sensing task and is more likely to be able to undertake the task. The results show that under the arrival rates of 40 task/min, 60 task/min and 100 task/min, the system recruits slightly less than the benchmark scheme.