PPO multi-objective task allocation method for heterogeneous crowd sensing
The task allocation of existing mobile crowd sensing systems is mainly carried out for a single type of mobile users,but there is a lack of research on the task allocation of heterogeneous crowd sensing where there are multiple types of mobile users.Therefore,we define the area accessibility of heterogeneous mobile users,and give a classification of sensing sub-regions.Then,we construct a multi-objective constrained optimization model for task allocation of dynamic heterogeneous crowd sensing systems,taking into account the time-varying nature of the number of sensing tasks and the size of mobile users.The model aims to maximize the sensing quality and minimize the sensing cost,taking into account the maximum number of tasks to be performed by users and the restricted working time of UAVs.To solve this optimization problem,a multi-objective evolutionary optimization algorithm based on proximal policy optimization is proposed.The proximal policy optimization is used to select the evolutionary operator with the highest reward value according to the current evolutionary state of the population,and generate the offspring population.The experimental results of comparing the proposed algorithm with various algorithms for different heterogeneous crowd sensing instances show that the optimal solution set of Pareto obtained by the proposed algorithm has the best convergence and distributivity,and the evolutionary operator selection strategy can effectively improve the adaptability to time-varying factors and improve the performance of the algorithm.
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