Prediction Based User Selection in Time-Sensitive Mobile Crowdsensing

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users, who provide the sensing services. The tasks, in practice, often have various spatiotemporal requirements, which make it hard to select suitable user set to perform the tasks. In this paper, we use the mobility prediction model to deal with this challenge and then propose the user selection algorithm to solve the user selection problem. From the perspective of mobility, it is probabilistic that the timesensitive tasks will be done on time and we use the time-related Markov model to achieve the probabilities. Furthermore, we consider the different uploading ways and obtain the corresponding probabilities, which could be used to propose a greedy user selection algorithm to select the suitable user set under the budget constraint. Extensive simulations have been conducted over two real-life mobile traces and the results prove the efficiency of our proposed algorithm.

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