基于模糊强化学习和果蝇优化的WSN数据聚合算法

在无线传感器网络中,传感器的能量时有限的,如果传感器的能量耗尽,那么无线传感网络的鲁棒性和寿命就会大大降低.因此,提出了基于模糊强化学习和果蝇优化的数据聚合机制,以最大限度地延长网络寿命,并进行高效数据聚合.首先,网格聚类用于簇的形成和簇头的选择,接着评估各个网格簇所有可能的数据聚合节点,然后采用模糊强化学习选取最佳数据聚合节点,最后利用果蝇优化算法动态定位整个无线传感网络的数据汇聚节点.仿真结果表明,提出的数据聚合方案在能耗和网络鲁棒性方面优于对比方案.

WSN Data Aggregation Algorithm Based on Fuzzy Reinforcement Learning and Fruit Fly Optimization

In a wireless sensor network, the sensor has limited energy. If it runs out of energy, the robustness and lifespan of the network will be greatly reduced. Therefore, a data aggregation mechanism based on fuzzy reinforcement learning and fruit fly optimization is proposed to maximize the lifespan of the network and perform efficient data aggregation. First, grid clustering is applied to cluster formation and cluster head selection. Then, all possible data aggregation nodes of each grid cluster are evaluated, in which the best one is selected by fuzzy reinforcement learning. Finally, the fruit fly optimization algorithm is adopted to dynamically position the data aggregation nodes of the entire wireless sensor network. The simulation results show that the proposed scheme is better than the comparison scheme in terms of energy consumption and network robustness.