Prediction and management in energy harvested wireless sensor nodes

Solar panels are frequently used in wireless sensor nodes because they can theoretically provide quite a bit of harvested energy. However, they are not a reliable, consistent source of energy because of the Sun's cycles and the everchanging weather conditions. Thus, in this paper we present a fast, efficient and reliable solar prediction algorithm, namely, Weather-Conditioned Moving Average (WCMA) that is capable of exploiting the solar energy more efficiently than state-of-the-art energy prediction algorithms (e.g. Exponential Weighted Moving Average EWMA). In particular, WCMA is able to effectively take into account both the current and past-days weather conditions, obtaining a relative mean error of only 10%. When coupled with energy management algorithm, it can achieve gains of more than 90% in energy utilization with respect to EWMA under the real working conditions of the Shimmer node, an active sensing platform for structural health monitoring.