# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 2 Related Work  
MCS systems are envisioned to be a part of an Internet of Things (IoT) ecosystem [21] where sensed data are acquired from mobile smart devices in a participatory or opportunistic manner. Collected data are reported to the MCS platform, which is, in fact, a sensing server located in a cloud platform, and undertakes data analytics and visualization tasks on the crowd-sensed data. Figure 1 illustrates the building blocks of a conventional MCS system. The end user of an MCS campaign reserves a limited budget to recruit and reward participants for their contribution. Therefore, it is vital to have a solid recruitment policy in order to minimize the costs and ensure that the participants who provide useful data will be recruited for the highest possible quality of the analytics result. In particular for mission-critical applications, selecting cooperative participants that are of utmost trustworthiness is of paramount importance [22; 23]. Designing the optimal solution for recruiting users and assigning tasks among selected users is a grand challenge in MCS systems. The objective of such a solution is to make a compromise between maximum user and platform utilities. Furthermore, in the presence of malicious or unreliable participants, the trustworthiness of crowd-sensed data is also critical, as it directly impacts the platform utility [24]. As battery limitation of smart mobile devices is a key concern for participants, data acquisition and user recruitment models that account for energy efficiency and energy modeling are emergent. The authors in [25] model the power consumption of a participating mobile device in an MCS application under three categories: power consumption in data transfer [19; 26], power consumption in sensing [27; 28] and power consumption in computation [29; 30]. The study in [27] proposes an effective energy-saving scheme in the recognition of human physical activities through machine learning algorithms such as the Hidden Markov Model (HMM) and k-Nearest-Neighbors (kNN). A similar study has been carried out by the authors in [28] on wearable sensors, particularly accelerometers and gyroscopes that provide raw sensor data to Convolutional Neural Networks (DCNN). [PERSON] et al. [31] consider the context of opportunistic sensing systems and propose a solution to minimize the cost and improve the accuracy of sensed data by  
Figure 1: Building blocks of a typical Mobile Crowd-Sensing (MCS) system. predicting user location via deterministic and stochastic mobility models [31]. Piggyback crowd-sensing is another well-known technique that can be utilized in MCS systems to ensure power efficiency [19; 32]. Piggyback crowd-sensing aims at predicting the active usage/interaction patterns of the users with their mobile devices in order to upload collected data to the central platform [17]. In [32], three greedy algorithms are proposed to find the optimal number of participants who are capable of fulfilling the assigned task, whereas historical records are utilized by the research in [19] to predict future user calls and find the area of sensing coverage. [PERSON] et al. [33] present a pricing scheme that is built upon a bargaining theory-based technique for assigning tasks in a time-dependent manner among the participants. In [25], an energy-efficient MCS strategy, namely the two-call-based approach, is proposed to exploit phone calls instead of mobile data plans for anonymous transmission of tasks. The first phase of two-call-based approach predicts whether a user is a potential participant for the upcoming campaigns. The second step simply leads the users with no tasks to a two-step decision making process that involves determining whether or not the system needs more sensing tasks in the first step, and in the second step, the system determines whether or not to select the corresponding user. In [34], the authors address the problem of energy-efficient selection of MCS participants based on the maximum residual battery.