# Predictive Power Management for Wind Powered Wireless Sensor Node
)1 College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China; [EMAIL_ADDRESS] (B.L. ); [EMAIL_ADDRESS] (F.Z. )1 College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China; libowenjfu@outlook. with its main purpose to matching the system energy consumption with the ambient energy generation, which enables \"Energy Neutral Operation (ENO)\" [4]. On the whole, the main research issue of power management in EH-WSN lies in two aspects: How to maximize the harvested energy and how to maximize the energy utilization efficiency. Hence, harvesting circuits designed for different energy sources has been deeply researched and the Maximal Power Point Tracking (MPPT) algorithm has been implemented in some studies [5; 6]. However, concerning the conventional harvesting module, which is always cumbersome and massy, EH-WSN systems with small physical size and constrained energy storage capacity (supercapacitor) are more suitable for future smart applications (such as smart forest and smart agriculture) [7; 8], and it is also our focus. Concerning this light-weighted smart EH-WSN, power management faces great challenges due to the small capacity of the supercapacitor and an ultralow energy harvesting rate, therefore the existing harvesting method and energy allocation strategy should adapt to the individual characteristic of environmental energy source, calculate the optimal working point efficiently, and reallocate the energy effectively. Besides the energy uncertainty, another concern is the interconnections of EH-nodes. For a smart EH-node with constrained resource, it is nontrivial to achieve energy efficient communication over varying wireless channel. Thus, the Transmission Power Control (TPC) function is needed to regulate the wireless transmission power based on the link quality. Here two indexes: Received Signal Strength Index (RSSI) and Packet Error Rate (PER), are often used as the indicator of wireless link quality [9; 10]. Therefore, by periodically exploring the link quality, the transmission power can be updated to maintain a stable RSSI or PER at the receiver. Unlike traditional EH systems, smart EH-WSN has more constrained resources. With variable environment (available ambient energy and wireless channel condition) and scarce hardware capability, designing a specialized power manager to achieve a low power failure rate and high energy-utilization efficiency becomes a challenging issue for a smart EH-WSN system. Here we study a novel predictive power management framework, which combines optimal working point, predictive energy allocation, and best-matching transmission power control for the EH-WSN system. The optimization purposes are to minimize the power failure time, as well as maximize the energy utilization efficiency under energy harvesting constraints. To the best of our knowledge, this is the first work to analyze energy management optimization in EH-WSN jointly from the above three perspectives. The remainder of paper is as follows: Section 2 discusses the related works; Section 3 describes the model of the EH-WSN system; Section 4 presents the details of the power management strategy; Section 5 uses real-world wind energy profile to evaluate the performance; Section 6 concludes the paper.