College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.
Sensors (Basel). 2020 Feb 12;20(4):990. doi: 10.3390/s20040990.
Portable meteorological stations are widely applied in environment monitoring systems, but they are always limited in power-supplying due to no cable power, especially in long-term monitoring scenarios. Reducing power consumption by adjusting a suitable frequency of sensor acquisition is very important for wireless sensor nodes. The regularity of historical environment data from a monitoring system is analyzed, and then an optimization model of an adaptive genetic algorithm for environment monitoring data acquisition strategies is proposed to lessen sampling frequency. According to the historical characteristics, the algorithm dynamically changes the recent data acquisition frequency so as to collect data with a smaller acquisition frequency, which will reduce the energy consumption of the sensor. Experiment results in a practical environment show that the algorithm can greatly reduce the acquisition frequency, and can obtain the environment monitoring data changing curve with less error compared with the high-frequency acquisition of fixed frequency.
便携式气象站广泛应用于环境监测系统中，但由于没有电缆电源，它们在供电方面一直受到限制，尤其是在长期监测场景中。通过调整传感器采集的合适频率来降低功耗对无线传感器节点非常重要。通过分析监测系统中历史环境数据的规律，提出了一种环境监测数据采集策略自适应遗传算法优化模型，以减少采样频率。根据历史特征，该算法动态改变最近的数据采集频率，以便以较小的采集频率采集数据，从而降低传感器的能耗。在实际环境中的实验结果表明，该算法可以大大降低采集频率，并且与固定频率的高频采集相比，该算法可以用更少的错误获取环境监测数据的变化曲线。
