Design of an IoT System for Machine Learning Calibrated TDS Measurement in Smart Campus.

WF-IoT(2021)

引用 6|浏览0
暂无评分
摘要
This paper focuses on designing a low-cost and robust IoT-based TDS measurement system for the smart campus. The objective of this low-cost design problem is to find a solution that guarantees precise and uninterrupted output data. The dynamic reading of data, storage capacity, and calibration errors of sensors are the major challenges for IoT-based TDS measurement systems. These challenges are combated in the proposed design using a non-invasive mechanism for data collection, wireless connectivity to the data server, and machine learning calibration of sensor nodes. The TDS data of various water stations located inside the campus is used for the experimental study to develop a regression model for temperature compensation and calibration. The value of TDS sensor voltage variation against temperature is analyzed. The evaluation of the model was performed based on the R 2 and the root mean square error. By using 3 rd degree polynomial regression, we have obtained an R 2 value of 93.96 % and an RMSE of 27.93.
更多
查看译文
关键词
Smart Campus,IoT,Water Quality,TDS measurement,Temperature Compensation,Machine Learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要