Abstract
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.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The proposed method found that support vector regression (SVR) can improve prediction accuracy by 21% and save
To improve on the power efficiency and accuracy, a multi-step measurement mechanism and power self-optimizing process are proposed in this paper
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the continuous improvement of wireless devices, more and more wireless applications have been proposed to improve human life quality. To acquire and monitor human body signals for healthcare purposes, wireless body sensor network (WBSN) was proposed in [1]. The wireless sensors, or wireless devices, were strategically positioned to allow convenience and comfort in acquiring and monitoring the body signals. The smart city was defined by IBM to enhance the wider aspects of human living. The technology is the key factor of building a smart city with integrated software, hardware, and networks. The architecture of a smart city is composed of three layers, namely, perception, network, and application, which were illustrated in [3]. The analyzed results can be applied to various fields such as in transportation, health care, education, energy management, and environment, etc
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