Energy modeling and adaptive sampling algorithms for energy‐harvesting powered nodes with sampling rate limitations

This article explores the implementation of different sampling strategies for a practical energy‐harvesting wireless device (sensor node) powered by a rechargeable battery. We look for a realistic yet effective sampling strategy that prevents packet delivery failures, which is simple enough to be implemented in low‐complexity hardware. The article proposes methods that balance erratic energy arrivals and include advantages of dynamic data‐driven approaches based on historical data. Due to the industrial requirements in terms of minimum acceptable sampling frequency, we also integrate sampling rate limits and verify the proposed methods. To do so, we simulated the operation of an industrial data logger powered with a solar panel relying on the enhanced state of the model for battery charging. Finally, the proposed methods are compared based on energy consumption over a year and the amount of packet delivery failures, thus showing how some modifications of available strategies achieve satisfactory performance in this sense