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The Role of Energy Reservoirs in Distributed Computing : = Manufacturing, Implementing, and Optimizing Energy Storage in Energy-Autonomous Sensor Nodes.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	The Role of Energy Reservoirs in Distributed Computing :/
其他題名:	Manufacturing, Implementing, and Optimizing Energy Storage in Energy-Autonomous Sensor Nodes.
作者:	Cowell, Martin Andrew.
面頁冊數:	1 online resource (129 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
標題:	Energy. -
電子資源:	click for full text (PQDT)
ISBN:	9780355576573

The Role of Energy Reservoirs in Distributed Computing : = Manufacturing, Implementing, and Optimizing Energy Storage in Energy-Autonomous Sensor Nodes.
Cowell, Martin Andrew.

The Role of Energy Reservoirs in Distributed Computing :Manufacturing, Implementing, and Optimizing Energy Storage in Energy-Autonomous Sensor Nodes. - 1 online resource (129 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2017.

Includes bibliographical references

The world already hosts more internet connected devices than people, and that ratio is only increasing. These devices seamlessly integrate with peoples lives to collect rich data and give immediate feedback about complex systems from business, health care, transportation, and security. As every aspect of global economies integrate distributed computing into their industrial systems and these systems benefit from rich datasets. Managing the power demands of these distributed computers will be paramount to ensure the continued operation of these networks, and is elegantly addressed by including local energy harvesting and storage on a per-node basis. By replacing non-rechargeable batteries with energy harvesting, wireless sensor nodes will increase their lifetimes by an order of magnitude.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355576573Subjects--Topical Terms:

784773
Energy.
Index Terms--Genre/Form:

554714
Electronic books.

The Role of Energy Reservoirs in Distributed Computing : = Manufacturing, Implementing, and Optimizing Energy Storage in Energy-Autonomous Sensor Nodes.
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Paul K. Wright.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2017.
504 $a Includes bibliographical references
520 $a The world already hosts more internet connected devices than people, and that ratio is only increasing. These devices seamlessly integrate with peoples lives to collect rich data and give immediate feedback about complex systems from business, health care, transportation, and security. As every aspect of global economies integrate distributed computing into their industrial systems and these systems benefit from rich datasets. Managing the power demands of these distributed computers will be paramount to ensure the continued operation of these networks, and is elegantly addressed by including local energy harvesting and storage on a per-node basis. By replacing non-rechargeable batteries with energy harvesting, wireless sensor nodes will increase their lifetimes by an order of magnitude.
520 $a This work investigates the coupling of high power energy storage with energy harvesting technologies to power wireless sensor nodes; with sections covering device manufacturing, system integration, and mathematical modeling. First we consider the energy storage mechanism of supercapacitors and batteries, and identify favorable characteristics in both reservoir types. We then discuss experimental methods used to manufacture high power supercapacitors in our labs. We go on to detail the integration of our fabricated devices with collaborating labs to create functional sensor node demonstrations.
520 $a With the practical knowledge gained through in-lab manufacturing and system integration, we build mathematical models to aid in device and system design. First, we model the mechanism of energy storage in porous graphene supercapacitors to aid in component architecture optimization. We then model the operation of entire sensor nodes for the purpose of optimally sizing the energy harvesting and energy reservoir components. In consideration of deploying these sensor nodes in real-world environments, we model the operation of our energy harvesting and power management systems subject to spatially and temporally varying energy availability in order to understand sensor node reliability. Looking to the future, we see an opportunity for further research to implement machine learning algorithms to control the energy resources of distributed computing networks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Energy. $3 784773
650 4 $a Electrical engineering. $3 596380
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