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Improving energy predictions in EH-WSNs with Pro-Energy-VLT

Authors:

Alessandro Cammarano,

Chiara Petrioli,

Dora SpenzaAuthors Info & Claims

SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

Article No.: 41, Pages 1 - 2

https://doi.org/10.1145/2517351.2517413

Published: 11 November 2013 Publication History

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Abstract

The increasing popularity of micro-scale energy-scavenging techniques for wireless sensor networks (WSNs) is opening new opportunities for the development of energy-autonomous systems. To sustain perpetual operations, however, environmentally-powered motes must adapt their workload to the stochastic nature of ambient power sources. Energy prediction algorithms, which forecast the source availability and estimate the expected energy intake in the near future, are precious tools to support the development of proactive power management strategies. In this work, we propose Pro-Energy-VLT, an enhancement of the Pro-Energy prediction algorithm that improves the accuracy of energy predictions, while reducing its memory and energy overhead.

References

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Cited By

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Zazoua EAjib WBoukadoum M(2023)Deep Learning-Based Receiver Energy Prediction in Energy Harvesting Wireless Sensor Network2023 IEEE 14th Latin America Symposium on Circuits and Systems (LASCAS)10.1109/LASCAS56464.2023.10108110(1-5)Online publication date: 27-Feb-2023
https://doi.org/10.1109/LASCAS56464.2023.10108110
Ge YNan YBai L(2019)A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor NetworksEnergies10.3390/en1224476212:24(4762)Online publication date: 13-Dec-2019
https://doi.org/10.3390/en12244762
Adu-Manu KAdam NTapparello CAyatollahi HHeinzelman W(2018)Energy-Harvesting Wireless Sensor Networks (EH-WSNs)ACM Transactions on Sensor Networks10.1145/318333814:2(1-50)Online publication date: 27-Apr-2018
https://dl.acm.org/doi/10.1145/3183338
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Index Terms

Improving energy predictions in EH-WSNs with Pro-Energy-VLT
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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Published In

SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

November 2013

443 pages

ISBN:9781450320276

DOI:10.1145/2517351

General Chair:
Chiara Petrioli
University of Rome `La Sapienza'
,
Program Chairs:
Landon Cox
Duke University
,
Kamin Whitehouse
University of Virginia

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 11 November 2013

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SenSys '13: The 11th ACM Conference on Embedded Network Sensor Systems

November 11 - 15, 2013

Roma, Italy

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SenSys '13 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Zazoua EAjib WBoukadoum M(2023)Deep Learning-Based Receiver Energy Prediction in Energy Harvesting Wireless Sensor Network2023 IEEE 14th Latin America Symposium on Circuits and Systems (LASCAS)10.1109/LASCAS56464.2023.10108110(1-5)Online publication date: 27-Feb-2023
https://doi.org/10.1109/LASCAS56464.2023.10108110
Ge YNan YBai L(2019)A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor NetworksEnergies10.3390/en1224476212:24(4762)Online publication date: 13-Dec-2019
https://doi.org/10.3390/en12244762
Adu-Manu KAdam NTapparello CAyatollahi HHeinzelman W(2018)Energy-Harvesting Wireless Sensor Networks (EH-WSNs)ACM Transactions on Sensor Networks10.1145/318333814:2(1-50)Online publication date: 27-Apr-2018
https://dl.acm.org/doi/10.1145/3183338
Ahmed FTamberg GLe Moullec YAnnus P(2017)Dual-Source Linear Energy Prediction (LINE-P) Model in the Context of WSNsSensors10.3390/s1707166617:7(1666)Online publication date: 20-Jul-2017
https://doi.org/10.3390/s17071666
Muhammad Qureshi HSaleem USaleem MPitsillides ALestas M(2017)Harvested Energy Prediction Schemes for Wireless Sensor NetworksWireless Communications & Mobile Computing10.1155/2017/69283252017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/6928325
Ateniese GBianchi GCapossele APetrioli CSpenza D(2017)Low-Cost Standard Signatures for Energy-Harvesting Wireless Sensor NetworksACM Transactions on Embedded Computing Systems10.1145/299460316:3(1-23)Online publication date: 28-Apr-2017
https://dl.acm.org/doi/10.1145/2994603
Cammarano APetrioli CSpenza D(2016)Online Energy Harvesting Prediction in Environmentally Powered Wireless Sensor NetworksIEEE Sensors Journal10.1109/JSEN.2016.258722016:17(6793-6804)Online publication date: Sep-2016
https://doi.org/10.1109/JSEN.2016.2587220
Shaikh FZeadally S(2016)Energy harvesting in wireless sensor networks: A comprehensive reviewRenewable and Sustainable Energy Reviews10.1016/j.rser.2015.11.01055(1041-1054)Online publication date: Mar-2016
https://doi.org/10.1016/j.rser.2015.11.010
Porcarelli DSpenza DBrunelli DCammarano APetrioli CBenini L(2015)Adaptive Rectifier Driven by Power Intake Predictors for Wind Energy Harvesting Sensor NetworksIEEE Journal of Emerging and Selected Topics in Power Electronics10.1109/JESTPE.2014.23165273:2(471-482)Online publication date: Jun-2015
https://doi.org/10.1109/JESTPE.2014.2316527

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Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

Efficient flow-control algorithm cooperating with energy allocation scheme for solar-powered WSNs

Prediction free energy neutral power management for energy harvesting wireless sensor nodes

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Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

Efficient flow-control algorithm cooperating with energy allocation scheme for solar-powered WSNs

Prediction free energy neutral power management for energy harvesting wireless sensor nodes

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