An allocation method of crowdsourcing tasks for protecting fairness of participants

With the increasingly powerful functions of mobile devices and the popularity of wireless networks, spatial crowdsourcing has become a new service model. This paper studies the task assignment problem in spatial crowdsourcing, which reduces the commuting cost of participants while ensuring the fairness of participants under the condition of task execution order constraints. This paper first proposes the Fairness Monte Carlo Tree Search (FMCTS) task allocation algorithm, ensuring that the maximum attendance difference of participants is below the set value while reducing the participants' commuting costs. After that, to optimize the efficiency of the algorithm, a FMCTS-Guided (FMCTSG) task allocation algorithm is proposed, which can reduce the running time of the algorithm. Finally, for non-emergency tasks, a Delayed FMCTS (DFMCTS) algorithm is proposed, which can further reduce the commuting cost of task participants. The experimental results show that the task allocation method proposed in this paper can effectively reduce the commuting cost of participants while ensuring the fairness of participants.