Crowdsourcing on mobile cloud: Cost minimization of joint data acquisition and processing

As the advance of mobile devices, crowdsourcing has been successfully applied in many scenarios by employing distributed mobile devices to collectively monitor a diverse range of human activities and surrounding environment. Unfortunately, treating mobile devices as simple sensors that generate raw sensing data may lead to low efficiency because of excessive bandwidth occupation and additional computation resource consumption. In this paper, we integrate crowdsourcing into existing mobile cloud framework such that data acquisition and processing can be conducted in a uniform platform. We consider a dynamic network where mobile devices may join and leave the network at any time. To deal with the challenges of sensing and computation task assignment in such a dynamic environment, we propose an online algorithm with the objective of minimizing the total cost including sensing, processing, communication and delay cost. Extensive simulations are conducted to demonstrate that the proposed algorithm can significantly reduce the total cost of crowdsourcing.