Harvesting-aware energy management for environmental monitoring WSN

An intelligent energy controller is proposed to manage operation of wireless sensor nodes equipped with energy harvesting devices. The energy controller uses Takagi-Sugeno fuzzy logic and has inputs for the state of the energy buffer and forecasts of solar energy available for harvest. Two different forecasting horizons were investigated, current and next-day, using ideal and pressure-based forecasts. Differential evolution is used to optimize the controller. To validate the evolved controller, a wireless sensor network is simulated using real field-collected environmental data. The optimization goal is to best utilize the solar energy available for harvest while preserving a backup energy reserve. Performing the highest number of operations possible while leaving the energy reserve intact increases deployment time and reliability. The controller using current and next-day energy forecasts made better use of the available energy, indicated by a lower fitness function. However, while it took more measurements when compared to the controller only using the current-day forecast, it also used more reserve energy while still remaining at only a small fraction of the total available reserve. Reserve energy usage using the pressure-based forecast was higher for both forecasting horizons compared to the ideal energy forecast, pointing to further performance improvements possible for a more accurate forecast.