Design and Development of Multi-Sensor ADEP for Bore Wells Integrated with IoT Enabled Monitoring Framework

Author:

K Sakthidasan @ Sankaran1,J Lekha2,M Jenath3,Easwaran Balamurugan4

Affiliation:

1. Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai, India.

2. Department of Data Science, Christ Deemed to be University, Pune Lavasa Campus, Maharashtra, India.

3. Electronics and Communication Engineering, Sri Sairam Egineering College, Chennai, India.

4. Texila American University, 405 A, Greater lubinda Road, Lilayi, Lusaka, Zambia.

Abstract

Typically, about 51% of the groundwater satisfies the drinking water worldwide and is regarded as the major source for the purpose of irrigation. Moreover, the monitoring and assessment of groundwater over bore wells is essential to identify the effect of seasonal changes, precipitations, and the extraction of water. Hence, there is a need to design a depth sensor probe for bore wells so as to analyze/monitor the quality of underground water thereby estimating any geophysical variations like landslides/earthquakes. Once the depth sensor probe is designed, the data is collected over wireless sensor network (WSN) medium and is stored in cloud for further monitoring and analyzing purposes. WSN is the major promising technologies that offer the real-time monitoring opportunities for geographical areas. The wireless medium in turn senses and gathers data like rainfall, movement, vibration, moisture, hydrological and geological aspects of soil that helps in better understanding of landslide or earthquake disasters. In this paper, the design and development of geophysical sensor probe for the deep bore well so as to monitor and collect the data like geological and hydrological conditions. The data collected is then transmitted by wireless network to analyze the geological changes which can cause natural disaster and water quality assessment.

Publisher

Anapub Publications

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Lumpy Skin Disease Prediction Using Machine Learning;2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT);2024-01-04

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