Solar energy harvesting prediction algorithms: WCMA下载

weixin_39820835 2019-09-18 05:00:19

这是太阳能采集预测算法的主流算法，WCMA。用的是matlab进行仿真的
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这是太阳能采集预测算法的主流算法，WCMA。用的是matlab进行仿真的

Energy harvesting wireless sensor network (EH-WSN) is promising in applications, however the frequent occurrence of temporal death of nodes, due to the limited harvesting capability, presents a difficulty in meeting the quality-of-service requirements of the network. For a node with temporal death in an EH-WSN, this paper presents a new model, which consists of, a Markov model to trace the energy harvesting process, a queuing analytical model to model the working mechanism of the sensor node and a continuous fluid process to capture the evolution of the residual energy in the EH-WSN node. Using the Markov fluid queue (MFQ) theory, we discuss various performance aspects of the EH-WSN node with temporal death, including the temporal death occurrence probability, the probability density of the residual energy, the stationary energy consumption, the queue length distribution in the data buffer, the packet blocking probability, and so on. In order to obtain the dropping probability of a given packet, based on the structure of the MFQ, we develop an auxiliary MFQ and derive the formulations of two types of the packet dropping probabilities, i.e., the packet dropping probability due to energy depletion and that due to channel error. Numerical examples are provided to illustrate the theoretical findings, and new insights into understanding the impacts of the parameters on the performance metrics are presented.

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner.While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes.We consider the above issues in power management for energy-harvesting sensor networks.We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

RF-based Energy Harvesting in Decode-and-Forward Relaying Systems_ Ergodic and Outage Capacities PDF论文

Sensors and Instrumentation, Aircraft Aerospace and Energy Harvesting 2019 Sensors and Instrumentation, Aircraft Aerospace and Energy Harvesting 2019.rar (14.91 MB, 下载次数: 446 ) ISSN 2191-5644 ISSN 2191-5652 Conference Proceedings of the Society for Experimental Mechanics Series ISBN 978-3-319-74641-8 ISBN 978-3-319-74642-5 Library of Congress Control Number: 2018941837 © The Society for Experimental Mechanics, Inc. 2019 本主题由 jackzhan

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