SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration into the IoT ecosystem with their built-in sensors, particularly in Mobile Crowd-Sensing (MCS) campaigns. The MCS systems achieve the objectives of the large-scale
... ted sensing concept in the IoT if a sufficient number of participants are engaged to the collaborative data acquisition process. Therefore, user recruitment is a key challenge in MCS, which requires effective incentivization of cooperative, truthful and trustworthy users. A grand concern for the participants is the battery drain on the mobile devices. It is a known fact that battery drain in a smartphone is a function of the user activity, which can be modeled under various contexts. With this in mind, we propose a new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). SOBER-MCS uses sociability and the residual power of the participant smartphones as two primary criteria in the selection of participating devices. The former is an indicator of the participant willingness toward sensing campaigns, whereas the latter is used to prioritize personal use over crowd-sensing under critical battery levels. We use sociability profiles that were obtained in our previous work and use those values to simulate the sociability behavior of a large pool of participants in an MCS environment. Through simulations, we show that SOBER-MCS is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively. Participants of an MCS campaign offer multi-sensory data acquired from personalized smart devices, which normally include GPS, gyroscope, microphone, light, camera, accelerometer and wireless communication interfaces [11, 12] . Data acquired from multiple participants are aggregated and further analyzed at a central platform. Data acquisition in MCS can be performed in either of two forms: participatory or opportunistic  . The former seeks the active involvement of participants in the sensing process, whereas the latter minimizes participant involvement since applications or devices are the decisive drivers for sensing campaigns. In both opportunistic and participatory sensing, user recruitment requires the involvement of a centralized platform that accounts for various criteria in the selection of users, as well as participant-task matching  . Since no upfront infrastructural investment is required, MCS can achieve effective acquisition of sensory data and leads to accurate and precise analysis if user participation is elevated. Therefore, user recruitment is a grand challenge in participatory MCS systems. Designing suitable algorithms to address the utility trade-off problem between the participants and the MCS platform has been an open issue in the literature since the MCS concept was initially coined  . At both the platform and user ends, performance evaluation involves optimization of incomes and costs. The income of the platform is related to the value of the sensed data, and it is defined by the Sensing as a Service (S 2 aaS) business models  , whereas the compensation of the users for their contributions translates into the platform cost. User costs in the participation of data aggregation are basically the power drained from the batteries for sensing the tasks and cellular data usage/charges in case sensor readings are reported via a cellular network interface. Energy consumption or battery drain, along with other concerns, is one of the main parameters that contributes to the user incentives due to the power-hungry nature of the mobile devices in the MCS environment . Hence, effective incentives in the acquisition of crowd-sensed data should aim at maximizing the benefits (i.e., utility) at both the user and platform ends and minimizing energy costs at the user end  . The advances in data mining and machine learning enable the development of predictive models for the discovery of events, communities and knowledge [18, 19] . Thus, introducing such intelligence to the MCS system could reveal behavioral patterns of users and can consequently help to ameliorate platform costs. In , a mobile behaviometric framework is proposed to assess users' activity on social networking platforms and introduce sociability metrics to generate fingerprints of the online social behavior of users. Through various machine learning algorithms, various profiles and usage patterns are discovered. The discovered information in that study is invaluable for an MCS study since it can be used in user recruitment to meet the two objectives that were mentioned above. In this article, we aim to cope with the battery drains of the participants in an MCS system. To this end, we propose a novel methodology for participant recruitment for participatory data acquisition in MCS systems by introducing a multi-context user recruitment policy into user incentives, which is based on two indicators: (i) user sociability, which denotes an indication of willingness for participation in crowd-sensing campaigns, and (ii) the energy, which denotes the residual battery level of mobile devices. The proposed scheme is called Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). The sociability-awareness in participant recruitment enforces the recruitment of active users, whereas energy-awareness enables prioritizing personal use of the participant devices in lieu of sharing built-in sensors when the residual battery level drops below a certain threshold. We run extensive simulations for the performance evaluation of SOBER-MCS and show that the multi-context user recruitment can improve battery consumption up to 18.5% while improving the user and platform utilities up to 12% and 20%, respectively. The article is organized as follows. In Section 2, we present the related work and background on the subject. Section 3 presents the proposed recruitment model in detail. The performance evaluation of the proposed solution is presented in Section 4. Finally, Section 5 concludes the article and provides future directions.