Online energy assessment with supercapacitors and energy harvesters

Combining energy harvesting with energy-aware load adaptation and scheduling enables perpetually operating sensor networks. The practical realization of this goal yet requires methods for reliable and precise holistic online energy assessment. While the building blocks—assessing residual energy, predicting energy intake, and tracing energy consumption—have been studied in detail, the analysis of their interaction on a real platform has been neglected. This paper answers the question, whether these techniques can be easily joined to give a precise and correct picture of a sensor node's energetic state and behavior. For this purpose, we model the energy flow of a prototype energy-harvesting and supercapacitor-powered sensor node. We show that in a real deployment simple supercapacitor models suffice for energy assessment, while capacity calibration is mandatory yet practicable. We evaluate the joint performance of state-of-the-art energy assessment based on an outdoor field test. We verify the system model and show the feasibility of holistic online energy assessment, which tolerates small configuration errors, achievable with a combination of generic configuration and online calibration. We analyze the feasibility of forecasting a node's future energy reserve and find that the presented method produces accurate results for uniformly distributed consumption profiles.