Abstract
A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.
Highlights
The Internet of Things (IoT) is an emerging paradigm that aims to provide reliable access to heterogeneous and distributed data and may represent a good solution for the smart lives of the future
Concerning the conventional harvesting module, which is always cumbersome and massy, Energy Harvesting (EH)-Wireless Sensor Network (WSN) systems with small physical size and constrained energy storage capacity are more suitable for future smart applications [7,8], and it is our focus. Concerning this light-weighted smart Energy Harvesting-WSN (EH-WSN), power management faces great challenges due to the small capacity of the supercapacitor and an ultralow energy harvesting rate, 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
With variable environment 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
Summary
The Internet of Things (IoT) is an emerging paradigm that aims to provide reliable access to heterogeneous and distributed data and may represent a good solution for the smart lives of the future. 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 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, 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. 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. 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
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