Chi Tsun-Kuang, Chen Hsiao-Chi, Chen Shih-Lun, Abu Patricia Angela R
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan.
Department of Business Administration, Chung Yuan Christian University, Taoyuan City 320314, Taiwan.
Sensors (Basel). 2021 Mar 10;21(6):1936. doi: 10.3390/s21061936.
In this paper, a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance, the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16-0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device, an ultrasonic sensor module, a LORA transmission module, and a stepper motor. According to the experimental results, the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications.
本文针对智慧城市应用提出了一种新颖的自优化水位监测方法。考虑到系统维护，功耗效率和准确性对于物联网（IoT）设备和系统至关重要。本研究提出了一种多步测量机制和功率自充电过程，以提高水位监测应用设备的效率。通过移动传感器来估计相对于不同位置的距离，所提出的方法将精度提高了0.16 - 0.39%。在执行多步测量时会产生额外的电量，而功率自优化过程会动态调整设置以平衡充电和放电电流。在稳定充电模拟中，电池电量可以有效地超过50%。这些方法通过使用嵌入式控制设备、超声波传感器模块、LORA传输模块和步进电机成功实现。根据实验结果，所提出的多步方法对于水位监测应用具有高精度和高效功耗的优点。
