Abstract
Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.
Highlights
IntroductionThe present project of water quality monitoring has been primarily motivated by the fact that people in rural or undeveloped areas are at high risk of exposure to water-related diseases
The present project of water quality monitoring has been primarily motivated by the fact that people in rural or undeveloped areas are at high risk of exposure to water-related diseases.the problem is not limited to such regions
One cannot overstate the importance of remote environmental monitoring, since it can result in convenience and flexibility of observing environmental conditions from a distance, thereby reducing the risks, cost, and the required time, while improving the accuracy and efficiency
Summary
The present project of water quality monitoring has been primarily motivated by the fact that people in rural or undeveloped areas are at high risk of exposure to water-related diseases. It should be noted that will the sampling frequency have a significant impact on the energy usage, the processing and transmitting of the sampled data will consume extra energy With this in mind, the proposed data-driven adaptive sampling algorithm (DDASA) has been developed, which dynamically changes the sampling frequency based on the nature of the sampled signal. The proposed data-driven adaptive sampling algorithm (DDASA) has been developed, which dynamically changes the sampling frequency based on the nature of the sampled signal This algorithm should be universally applicable with respect to conserving energy and prolonging the lifetime of the WSN.
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