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Traditional crowdsensing platforms rely on sensory information collected from a group of independent users or sensors. Recently, socially aware crowdsensing services have been introduced as the integration of social networks and crowdsensing platforms. For example, in health-related crowdsensing applications, a user benefits from information regarding food, exercise, medicine, and medical treatment collected and shared by his/her socially connected friends and family members. In this article, we first introduce basic concepts of socially aware crowdsensing services and highlight the importance of "social network effects" in the services. Typically adopted in social networks, network effects are used to quantify the influence of an action or preference of one user on other users with social ties. With this focus, we then discuss important aspects of socially aware crowdsensing services with network effects and some technical challenges. We find that game theory is a suitable analytical tool to investigate such crowdsensing services, for which important related work is surveyed. To address existing research gaps, we propose a game model for an incentive mechanism design with incomplete information about social network effects in socially aware crowdsensing. The proposed model is shown to improve the benefits of the crowdsensing service provider as well as those of the users.

An Incentive Mechanism Design for Socially Aware Crowdsensing Services with Incomplete Information