# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 6 Conclusions  
The widespread availability of smartphones, personalized devices and wearables that are equipped with highly-capable built-in sensors makes these devices viable candidates for the non-dedicated sensing paradigm of which Mobile Crowd-Sensing (MCS) is a part. MCS has recently been an emerging concept for non-dedicated sensing [3]. Built-in sensors (e.g., GPS, camera, light, accelerometer, microphone and gyroscope) in personalized smart devices are already employed to operate in various contexts such as healthcare [4; 5; 6], environmental/traffic monitoring and control [7; 8; 9; 10] and public safety [43]. However, in the cloud-based Sensing as a Service (S\({}^{2}\)aaS) concept, the personal smart devices are considered to be significantly energy-hungry when they are integrated into a non-dedicated sensing ecosystem [16]. Therefore, cost-effective solutions in the data acquisition for MCS are emergent to lower the energy expenditures and incentivize users for participation [17]. To address this issue, in this paper, we have proposed to exploit user profiling to recruit participants in an intelligent way by assigning the task to the users with a high probability to accept the task based on their sociability (i.e., availability) and their mobile battery level. The proposed scheme is called Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). The contributions of SOBER-MCS are two-fold: (i) integration of social behavioral profiling into the recruitment process; (ii) introducing energy efficiency to the user recruitment and incentivization process with the ultimate goal of addressing platform versus user utility. These contributions have been achieved by integrating a machine intelligence component with the MCS engine to predict user availability through social activity and sociability factor features of a participant. Furthermore, by introducing a residual battery level threshold to the eligibility criterion, more devices remain in the system at the end of the sensing campaigns. Three different classes of user profiles have been identified by the profiling engine of SOBER-MCS: highly-active user with activity more than 70% during the time window, moderately-active user with approximately 50% of activity and least active user with activity of 15% in average. The user profiles are passed to the MCS simulation framework to investigate the impact of applying defined user profiles with their settings to the MCS participants. Through simulations, we have shown that by having an additional intelligence component to recruit users based on sociability signatures and residual energy profiles, it is possible to make a compromise between platform and user utilities by selecting a user with: (i) high sociability such that the user is very likely to accept the tasks; (ii) acceptable residual battery level so that the smartphone is less likely to fail during the sensing campaign. Furthermore,  
Figure 15: Number of breakout mobile phones: (**a**) mobile crowd-sensing benchmark; (**b**) SOBER-MCS with \(\Re=10\%\); (**c**) SOBER-MCS with \(\Re=15\%\); and (**d**) SOBER-MCS with \(\Re=20\%\). SOBER-MCS increases user satisfaction by balancing the total energy consumption of smartphones by the exclusion of users with low mobile battery levels from the sensing campaign. Moreover, by recruiting highly sociable (i.e., available) users, SOBR-MCS reduces the energy consumption overhead. Consequently, the reduced overhead reduces the bids, as well as the MCS platform costs due to rewards. We are currently investigating the impact of heterogeneous and selective sensing concepts on SOBER-MCS. Furthermore, introducing mobility prediction and GPS-less sensing to SOBER-MCS are also included in our future agenda. F.A. worked on the implementation of the simulations. The simulations have been verified by B.K. All authors wrote the paper collaboratively. F.A. created the illustrative images.