Adaptive Power Management in Energy Harvesting Systems

Moser,; Thiele,; Brunelli, Davide; Benini, Luca

doi:10.1109/date.2007.364689

Cited by 100 publications

(51 citation statements)

References 9 publications

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“…Moser et al, describe a scheme in which sensing and communication rates are adapted to the rate of harvested energy while ensuring that memory use is at an optimum level [69]. Such an adaptation is performed by means of linear programming.…”

Section: Rf Transceiversmentioning

confidence: 99%

Reincarnation in the Ambiance: Devices and Networks with Energy Harvesting

Prasad

Devasenapathy

Rao

et al. 2014

IEEE Commun. Surv. Tutorials

101

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Abstract-Miniaturization of devices with higher computational capacity coupled with advancement in communication technologies is driving the growth of deployment of sensors and actuators in our surroundings. To keep up the pace with this growth, these tiny, battery-powered devices need small-sized and high-energy density batteries for longer operation time, which calls for improvement in battery technologies. An alternative is to harvest energy from the environment. An important aspect of energy harvesting is that the devices go through birth and death cycle with respect to their power unlike battery powered ones. Another important aspect is that context information is also generated while devices harvest energy from their ambiance. In this article we provide a comprehensive study of various types of energy harvesting techniques. We then provide some models used in energy harvesting systems and the design of such systems. We also throw light on the power management and networking aspects of the energy harvesting devices. At the end we discuss the major issues and avenues for further research.

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Section: Rf Transceiversmentioning

confidence: 99%

Reincarnation in the Ambiance: Devices and Networks with Energy Harvesting

Prasad

Devasenapathy

Rao

et al. 2014

IEEE Commun. Surv. Tutorials

101

View full text Add to dashboard Cite

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“…Research on energy harvesting power management has recently gained lot of attention, especially in the field of WSNs [8,[18][19][20][21]. One of the earliest studies on energy harvesting power management can be found in reference [22].…”

Section: Power Management For Energy Harvesting Wsnmentioning

confidence: 99%

A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies

et al. 2012

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Wireless sensor networks (WSNs) require an extremely energy-efficient design. As sensor nodes have limited power sources, the problem of autonomy is crucial. Energy harvesting provides a potential solution to this problem. However, as current energy harvesters produce only a small amount of energy and their storage capacity is limited, efficient power management techniques must also be considered. In this article we address the problem of modeling and simulating energy harvesting WSN nodes with efficient power management policies. We propose furthermore a framework that permits to describe and simulate an energy harvesting sensor node by using a high level modeling approach based on power consumption and energy harvesting. The node architectural parameters as well as the on-line power management techniques will also be specified. Two new power management architectures will be introduced, taking into account energy-neutral and negative-energy conditions. Simulations results show that the throughput of a sensor node can be improved up to 50% when compared to a state of the art power management algorithm for solar harvesting WSN. The simulation framework is then used to find an efficient system sizing for a solar energy harvesting WSN node.

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“…Another early work on EHS is [15]. In [16], the authors propose the use of optimal model predictive controllers to maximize the utility of energy harvesting sensor nodes. In [17], the authors show that cooperative ARQ protocols can reduce the energy requirements from the sensors, making it easier for them to operate in an energyneutral mode.…”

Section: Introductionmentioning

confidence: 99%

Power Management and Data Rate Maximization in Wireless Energy Harvesting Sensors

Murthy

2009

Int J Wireless Inf Networks

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This paper considers the problem of power management and throughput maximization for energy neutral operation when using an energy harvesting sensor (EHS) to send data over a wireless link. The EHS is assumed to be able to harvest energy at a constant rate, and use a fixed part of the energy harvested in a slot for measuring the channel state. The rest of the energy harvested is available for transmission, however, it can be stored in an inefficient battery if it is not fully utilized. The key constraint that the EHS needs to satisfy is energy neutrality, i.e., the expected energy drawn from the battery should equal the expected energy deposited into the battery. In this scenario, two popular models for data transmission are contrasted: the constant bit rate (CBR) model and the variable bit rate (VBR) model. In the CBR model, it is assumed that the EHS are designed to transmit data at a constant rate (using a fixed modulation and coding scheme) but are power-controlled. In the VBR model, the EHS selects both the transmit power and the data rate of transmission in each slot based on the channel instantiation. A framework under which the system designer can optimize several parameters of the EHS that determine the average data rate performance when the channel is Rayleigh fading is developed. Using this framework, the two transmission schemes are contrasted. It is shown that, with the right choice of parameter settings, the CBR scheme can perform nearly as well as the VBR scheme at significantly lower complextiy. The usefulness and validity of the framework developed is illustrated through simulations for specific examples.

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Adaptive Power Management in Energy Harvesting Systems

Cited by 100 publications

References 9 publications

Reincarnation in the Ambiance: Devices and Networks with Energy Harvesting

Reincarnation in the Ambiance: Devices and Networks with Energy Harvesting

A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies

Power Management and Data Rate Maximization in Wireless Energy Harvesting Sensors

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