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Power management in a sensor network for automated water quality monitoring

University of British Columbia

Power management is crucial in remote environmental monitoring, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested for sustaining a monitoring system. 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. Based on reinforcement learning, this thesis develops and applies an adaptive sampling algorithm and duty cycling for power management in automated water quality monitoring with energy harvesting. The state of the water quality parameters in a water source such as a lake or river may change in an unpredictable manner (e.g., may remain stable or change abruptly) depending on many factors such as climate or environmental changes or those caused by humans (e.g., waste water discharge from factories, construction, farming, and litter). Ideally, the sampling rate that is used for a sensor signal should depend on the rate at which the signal changes. Hence, adaptive sampling scheme using reinforcement learning is used in the present work, for water quality monitoring. The energy consumption for signal acquisition, processing, and transmission all depend on the sampling frequency, either directly or indirectly. Hence, it is desirable for the sensor nodes to dynamically learn how to determine the best sampling frequency for a sensor signal, depending how the signal changes due to the environmental situations, and adjust the sampling rate accordingly. It is found that by dynamically changing the sampling frequency, the battery state can be maintained at an energy-neutral level. Duty cycling also contributes to achieving the same goal by scheduling the working and sleeping time of a sensor node. It is shown that by switching between the work mode and the sleep mode, a satisfactory battery state can be maintained. These two methods have different degrees of advantage and performance in power management, but it is shown that both methods can achieve the energy neutrality while maintaining a high level of accuracy in the acquired data.

Text

2016-04-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/57922

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/