Energy‐prediction scheduler for reconfigurable systems in energy‐harvesting environment

Energy harvesting has been demonstrated to be a promising approach to mitigating energy constraints. Unlike battery‐based energy, available system energy significantly varies for energy‐harvesting systems. Partial dynamic reconfiguration is adopted as an effective approach for accelerating wireless sensor network (WSN) applications. Although a reconfigurable system can achieve better performance compared with software implementation, reconfiguration potentially requires a large amount of energy and time, particularly for cases where reconfiguration occurs frequently. To address this issue, a novel weather‐aware scheduler based on the weather‐conditioned moving average (WCMA) prediction algorithm is proposed in this study. To demonstrate the authors approach, a heterogeneous reconfigurable node is also proposed. The implementation of the proposed approach can improve reconfigurable system performance by up to 50% under energy‐harvesting conditions. The novelty of this work is 2‐fold. First, a prototype is adopted to demonstrate its efficiency for WSN. Second, a novel scheduler is proposed to manage hardware reconfiguration. In the scheduler, WCMA is used to predict future harvested energy.