Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications\n(e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing\nparadigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for\na single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can finish a part\nof the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore,\nthe probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task\ncrowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task\npartition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. Then, using a\nMarkov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task\npartition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workersâ??\narrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task\nassignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing\npotential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.
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