# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 3 Background
### Recruitment Algorithm  
This paper proposes a new participant recruitment approach, SOBER-MCS, that exploits sociability metrics of users to recruit them in a cost-effective manner and introducing energy-awareness into the sociability-awareness to ensure energy efficiency for the participant smartphones. At the core of SOBER-MCS lies the recruitment algorithm. Before proceeding with the details of the algorithm, Table 1 presents the notation used in this article to define the theoretical foundations of the proposal.  
As illustrated in Figure 3, upon the arrival of a sensing task, the MCS framework estimates the cost of the task in terms of potential energy consumption and shares this value with the users through the task package. Bidding starts upon receiving the task packages, and user bids are passed to the recruitment policy. The aim of the recruitment policy is to select the winners for the available task by aiming at minimum average battery drain for all participants due to participation in the MCS campaign.  
At the beginning, the system evaluates users' sociability using TrackMaison, which denotes a metric to estimate the participants' willingness to participate in sensing tasks. Sociability ranges from  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\mathcal{C}\) & Crowd-sensing campaign \\ \(w\) & Sensing task \\ \(SS\) & Set of sensors \(\mid s\in\ \mathcal{SS}\) \\ \(SS_{s}\) & sensor \(s\) \\ \(u\) & User \(u\) \\ \(\mathcal{U}\) & Set of users \(\mid u\in\mathcal{U}\) \\ \(\mathcal{W}\) & Set of winners \\ \(t\) & Timeslot \(t\) \\ \(T_{j}\) & Duration of task \(j\) \\ \(T_{prep}^{j}\) & Time (in second) needed to prepare phone to sense the task \(j\) \\ \(\mathcal{T}\) & Duration of the sensing task \\ \(T_{k}\) & \(k\)-th activity rate \\ \(L\) & Location of users and tasks \\ \hline \(\mathcal{A}_{sh}^{L_{trg_{s}}}\) & Short-term social activity of user \(u\) on app \(x\) \\ \(\mathcal{SF}_{sh}^{L_{trg_{s}}}\) & Short-term sociability factor of user \(u\) on app \(x\) \\ \(\mathcal{A}_{overall}^{L_{trg_{s}}}\) & Overall social activity \\ \(\mathcal{SF}_{overall}^{L_{trg_{s}}}\) & Overall sociability factor \\ \hline \(E_{i_{s}}^{f}\) & Energy factor of user \(i\) \\ \(E_{u_{j}}^{u_{j}}\) & Total energy consumed by user \(i\) to sense task \(j\) \\ \(E_{i_{t}}\) & Energy consumed by mobile device to run task \(j\) \\ \(E_{O}^{i_{t}}\) & Energy overhead consumed for user \(i\) to sense task \\ \hline \(P_{i_{t}}\) & Power usage of task \(j\) \\ \(P_{i_{t}}^{u_{j}}\) & power usage by user \(i\) to sense task \(j\) \\ \(P_{i_{t}}^{ss_{s}}\) & Power of sensors for task \(j\) \\ \(P_{O_{j}}^{u_{j}}\) & Overhead usage of user \(i\) to prepare phone to start sensing task \(j\) \\ \hline \(\alpha\) & Context-based parametric weight for running average calculation of activity rates \\ \(\beta\) & Context-based parametric weight for running average calculation of activity factors \\ \(\Re\) & Residual battery level threshold \\ \(\Re_{i}\) & Residual battery level of user \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of symbols. The notation in [41] has been partially adopted.  
0%-100% percent, and a higher percentage denotes a higher willingness for participation. TrackMaison acquires built-in sensors' data and statistical data regarding interaction patterns with multiple social network applications on smart mobile devices. TrackMaison then analyses the raw data and builds user profiles based on users' social activities using different machine learning algorithms.  
In the proposed sociability-aware recruitment policy, the MCS platform can predict the availability/willingness of participants by learning their historical data and estimates the energy requirement for the sensing task based on the task settings. Thus, the MCS platform can reduce the energy consumption due to participation by assigning the tasks to users that minimize the cost of participating in the MCS campaign.