NeTS: Small: Enabling High-Quality Mobile Crowdsourcing with Lifestyle-aware and Energy-efficient Control

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The success of a crowdsourcing application highly depends on whether a capable crowd can be recruited to undertake the corresponding tasks. For most applications, the cost is also a big concern. Recent research has been focused on incentive mechanisms for mobile crowdsourcing, which determine how to recruit a crowd mainly based on their prices/costs. However, scant attention has been paid to capability of the recruited crowd, i.e., the quality of services/data each individual mobile user and the whole crowd are potentially capable of providing. The project team aims to design a new lifestyle-aware approach for mobile crowdsourcing. The basic idea is to maintain a (relatively) stable set of mobile users and learn to gain a comprehensive view of their life patterns (i.e., their capabilities) such that once a mobile crowdsourcing task is received, a good crowd, whose capabilities well match the task, can be quickly found. The proposed research will significantly improve quality of mobile crowdsourcing services and reduce their energy usages, which can substantially benefit mobile crowdsourcing service providers and users, and more importantly, is good for global environment. Moreover, the proposed project is expected to fill the literature gap and inspire more research along this line.The objective of this project to enable high-quality mobile crowdsourcing by designing a novel and holistic solution for crowd recruitment, which utilizes an energy-efficient framework for learning lifestyles of mobile users via smartphone sensing, and then employs lifestyle-aware algorithms and incentive mechanisms for crowd recruitment. To achieve the above objective, the proposed research is organized into three cohesive research thrusts: Thrust 1 Energy-efficient Lifestyle Learning: a unified and energy-efficient framework will be developed to learn lifestyles of mobile users by characterizing their behaviors and habits, and predicting their future activities; Thrust 2 Lifestyle-aware Crowd Recruitment and Incentive Mechanisms: Quality of Crowd (QoC) models and lifestyle-aware algorithms will be developed for effective crowd recruitment; Thrust 3 Implementation and Performance Evaluation: the proposed approaches will be implemented on a smartphone sensing platform and will be validated and evaluated via extensive simulation and experiments.