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Smart River Monitoring Using Wireless Sensor Networks

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TLDR
This work employed the different types of wireless sensor nodes to monitor the water quality in real time at the Weija intake in the Greater Accra Region of Ghana and showed a significant effect on plant and aquatic life.
Abstract
Water quality monitoring (WQM) systems seek to ensure high data precision, data accuracy, timely reporting, easy accessibility of data, and completeness. The conventional monitoring systems are inadequate when used to detect contaminants/pollutants in real time and cannot meet the stringent requirements of high precision for WQM systems. In this work, we employed the different types of wireless sensor nodes to monitor the water quality in real time. Our approach used an energy-efficient data transmission schedule and harvested energy using solar panels to prolong the node lifetime. The study took place at the Weija intake in the Greater Accra Region of Ghana. The Weija dam intake serves as a significant water source to the Weija treatment plant which supplies treated water to the people of Greater Accra and parts of Central regions of Ghana. Smart water sensors and smart water ion sensor devices from Libelium were deployed at the intake to measure physical and chemical parameters. The sensed data obtained at the central repository revealed a pH value of 7. Conductivity levels rose from 196 S/cm to 225 S/cm. Calcium levels rose to about 3.5 mg/L and dropped to about 0.16 mg/L. The temperature of the river was mainly around 35°C to 36°C. We observed fluoride levels between 1.24 mg/L and 1.9 mg/L. The oxygen content rose from the negative DO to reach 8 mg/L. These results showed a significant effect on plant and aquatic life.

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A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

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A modified distance-based energy-aware (mDBEA) routing protocol in wireless sensor networks (WSNs)

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References
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Book

Persuasive Technology: Using Computers to Change What We Think and Do

B. J. Fogg
TL;DR: Mother Nature knows best--How engineered organizations of the future will resemble natural-born systems.
Journal ArticleDOI

Energy harvesting in wireless sensor networks: A comprehensive review

TL;DR: A comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs is presented and some of the challenges still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment are identified.
Journal ArticleDOI

A hybrid neural network and ARIMA model for water quality time series prediction

TL;DR: A hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks to provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions.
Journal ArticleDOI

Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey

TL;DR: A comprehensive review of the state-of-the-art technologies in the field of marine environment monitoring using wireless sensor networks using WSNs and some related projects, systems, techniques, approaches and algorithms is provided.
Journal ArticleDOI

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

TL;DR: The state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water quality Monitoring, and Disaster Monitoring, are reviewed to improve the ecosystem and human life.
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