Crowdsourcing energy-efficient participants to ensure quality-of-information

Crowdsourcing systems, by using smart devices like smartphones and iPad, have been widely used in various domains, but are currently facing new challenges. On one hand, different tasks offer different amount of incentive budgets in multitask systems, thus those tasks pay more should be satisfied preferentially. On the other hand, even if there are not enough budget for a sensing task, the system should also try to provide as much sensory data as possible to obtain potential clients. Furthermore, it is challenging to select an “optimal” set of participants as data contributors due to the above two points. In this paper, first, we introduce a metric to describe how the collected sensory data can quantify task´s the multi-dimensional data requirements, in terms of data distribution in spatiotemporal domains. Second, we propose a “task priority model” based on incentive budget, to explicitly quantify the relationship between the incentive budget usage and task priority. Then, we present a quality of information (QoI) aware participant selection approach as a suboptimal solution to the defined optimization problem. Finally, we compare our proposed scheme with existing methods via extensive simulations based on the real movement traces of ordinary citizens in Beijing. Extensive simulation results well justify the effectiveness and robustness of our approach.