Harvesting-Aware Energy Management for Environmental Monitoring WSN

James Rodway,Petr Musilek

doi:10.3390/en10050607

Abstract

Wireless sensor networks can be used to collect data in remote locations, especially when energy harvesting is used to extend the lifetime of individual nodes. However, in order to use the collected energy most effectively, its consumption must be managed. In this work, forecasts of diurnal solar energies were made based on measurements of atmospheric pressure. These forecasts were used as part of an adaptive duty cycling scheme for node level energy management. This management was realized with a fuzzy logic controller that has been tuned using differential evolution. Controllers were created using one and two days of energy forecasts, then simulated in software. These controllers outperformed a human-created reference controller by taking more measurements while using less reserve energy during the simulated period. The energy forecasts were comparable to other available methods, while the method of tuning the fuzzy controller improved overall node performance. The combination of the two is a promising method of energy management.

Highlights

Wireless sensor networks (WSN) can be used to collect data of interest with high spatial and temporal resolution
The meteorological data used in this study was obtained from a network of automated meteorological stations operated by the Washington State University (WSU) [17]
Node activity levels were updated at sunrises and sunsets

Summary

Introduction

Wireless sensor networks (WSN) can be used to collect data of interest with high spatial and temporal resolution. When outfitted with energy harvesting devices, these sensor nodes can be deployed without the need for large power supplies or frequent maintenance visits. Especially in environmental monitoring applications, certain energy sources are undesirable (e.g., large lead acid batteries that are difficult to transport) and cannot be utilized to increase deployment length [2]. Such applications include ecosystem monitoring in tropical dry forests, the arctic, and on glaciers [3,4,5]

Methods

Results

Conclusion