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Challenge: ultra-low-power energy-harvesting active networked tags (EnHANTs)

Authors:

Maria Gorlatova,

Ioannis Kymissis,

Dan Rubenstein,

Gil ZussmanAuthors Info & Claims

MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking

Pages 253 - 260

https://doi.org/10.1145/1614320.1614348

Published: 20 September 2009 Publication History

Abstract

This paper presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy devices that can be attached to objects that are traditionally not networked (e.g., books, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors. Recent advances in ultra-low-power wireless communications, ultra-wideband (UWB) circuit design, and organic electronic harvesting techniques will enable the realization of EnHANTs in the near future. In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is an order of magnitude higher than the energy required to transmit a bit). These special characteristics pose several new cross-layer research problems. In this paper, we describe the design challenges at the layers above the physical layer, point out relevant research directions, and outline possible starting points for solutions.

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Huang L(2020)Fast-Convergent Learning-Aided Control in Energy Harvesting NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2019.293634419:12(2793-2803)Online publication date: 1-Dec-2020
https://doi.org/10.1109/TMC.2019.2936344
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Index Terms

Challenge: ultra-low-power energy-harvesting active networked tags (EnHANTs)
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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cover image ACM Conferences

MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking

September 2009

368 pages

ISBN:9781605587028

DOI:10.1145/1614320

General Chairs:
Kang G. Shin
University of Michigan, USA
,
Yongguang Zhang
Microsoft Research Asia, China
,
Program Chairs:
Rajive Bagrodia
University of California, Los Angeles, USA
,
Ramesh Govindan
University of Southern California, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 September 2009

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MobiCom'09: Annual International Conference on Mobile Computing and Networking

September 20 - 25, 2009

Beijing, China

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Cited By

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https://dl.acm.org/doi/10.1145/3372154
Huang L(2020)Fast-Convergent Learning-Aided Control in Energy Harvesting NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2019.293634419:12(2793-2803)Online publication date: 1-Dec-2020
https://doi.org/10.1109/TMC.2019.2936344
Kedia TRatcliff JO’Connor MOluic SRose MFreeman JRainwater-Lovett K(2020)Technologies Enabling Situational Awareness During Disaster Response: A Systematic ReviewDisaster Medicine and Public Health Preparedness10.1017/dmp.2020.19616:1(341-359)Online publication date: 24-Aug-2020
https://doi.org/10.1017/dmp.2020.196
Chen KGao HCai ZChen QLi J(2019)Distributed Energy-Adaptive Aggregation Scheduling with Coverage Guarantee For Battery-Free Wireless Sensor NetworksIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737492(1018-1026)Online publication date: Apr-2019
https://doi.org/10.1109/INFOCOM.2019.8737492
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Chen TGhaderi JRubenstein DZussman GRubenstein DChen TGhaderi JZussman G(2018)Maximizing Broadcast Throughput Under Ultra-Low-Power ConstraintsIEEE/ACM Transactions on Networking10.1109/TNET.2018.280518526:2(779-792)Online publication date: 1-Apr-2018
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