In this paper, we introduce Smart Source, a crowd sourcing based mobile Question & Answer (Q&A) system that aims to provide mobile information seekers with timely, trustworthy and accurate answers while ensuring that information providers are not inappropriately burdened. We tackle this challenge by taking advantage of both static and dynamic context and semantics from mobile users (e.g., Geolocation, social network, expertise/interest, device sensor profiles, battery level) to identify sources of information (i.e., Workers) that are trusted by the user and accurate enough for the questions at hand. Given a question, the Smart Source broker middleware executes a scalable and efficient worker selection algorithm that uses a Lyapunov optimization framework to maximize the utility of worker selection while guaranteeing the stability of the overall system. An associated assignor selection is used to scale the selection process to a large number of users. We implement the Smart Source prototype system on an Android test bed and thoroughly evaluate the system using real world applications and data, in particular those that involve geospatial questions and answers. Evaluation results indicate that Smart Source is efficient and provides superior worker selection compared to baseline approaches. Smart Source is also highly customizable: it employs a general utility function and provides a control knob to tradeoff the optimality and responding time. We believe that Smart Source will pave a way for new mechanisms of interaction among mobile users.

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