Abstract

Mobile crowdsensing (MCS) refers to the process in which a collection of mobile devices share sensing data, which can then be aggregated to develop policies or applications that benefit a community. Traditionally, mobile crowdsensing has been deployed using a platform-centric task allocation approach. These platform-centric systems are primarily concerned with gaining profits for the platform by minimizing rewards given to users. Instead of this approach, this paper focuses on a more altruistic task allocation approach by proposing a user-centric task allocation approach. In this regard, we have developed two task allocation schemes: Nearest User Task Allocation (NUTA) and Nearest User Fair Task Allocation (NUFTA) in order to facilitate an optimized and equitable workload for the users.

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