Managing Energy-Harvesting Environmental Monitoring Systems

Abstract: Data collection is difficult in remote locations due to limited access and scarce resources. To power environmental monitoring equipment, and allow it to operate independently of maintenance and infrastructure, energy can be harvested from the environment; however, this makes the system dependent upon its environment and requires energy management to maintain performance. The objective of energy management in environmental monitoring is to produce the highest quality data set possible. These devices require simple, robust control, so a fuzzy inference system is used to produce a controller that maps device states to actions. To ensure that the fuzzy controller selects the best actions for each state, it is tuned by expert knowledge and genetic optimization. The simulation results from the Arctic and Boreal regions both show that these controllers allow energy consumption to be matched to the local energy profile, which improves performance over operating at a fixed level of energy consumption. By reducing the rate of data capture when energy is scarce, the monitor prevents failure and conserves energy. When energy is plentiful, the rate of data capture was increased to acquire high quality data. Managing energy and data storage allows the control system to delay energy-intensive operations, like wireless transmission, until environmental conditions became favourable. By combining energy management with suitable storage technologies, the vulnerability of the monitoring system is reduced to the point where storage can compensate for environmental energy scarcity and a suitable level of performance can be guaranteed for a deployment period of a few years.