边缘环境下基于移动群智感知计算卸载的数据汇聚.

The conventional cloud-end MCS system currently faces problems of excessive load, leading to a significant increase in delay and energy consumption during the data aggregation process, inevitably causing a decrease in data aggregation efficiency. To tackle this issue, this paper proposed a cloud-edge-end MCS computation offloading algorithm based on APDQN. Firstly, it established a utility function considering the balanced optimization of delay and energy consumption, with the maximization of system utility as an optimized goal. Secondly, improving the P-DQN algorithm, it proposed a computational offloading algorithm AP-DQN for combining resource allocation. This algorithm, leveraging the advantages of MCS, designated idle users as one of the offloading devices. Finally, the problem was solved using the proposed method. Experimental results show that, compared to existing algorithms, the proposed method significantly improves data aggregation efficiency and maintains excellent system stability. [ABSTRACT FROM AUTHOR]