research-article

Uncertainty-aware Energy Harvest Prediction and Management for IoT Devices

Authors:

Ganapati BhatAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems, Volume 28, Issue 5

Article No.: 87, Pages 1 - 33

https://doi.org/10.1145/3606372

Published: 09 September 2023 Publication History

Abstract

Internet of things (IoT) devices are popular in several high-impact applications such as mobile healthcare and digital agriculture. However, IoT devices have limited operating lifetime due to their small form factor. Harvesting energy from ambient sources is an effective method to supplement the battery. Energy harvesting necessitates development of energy management policies to manage the harvested energy. Designing optimal policies for energy management is challenging for two key reasons: (1) ambient energy sources are highly stochastic; therefore, energy management policies must consider the associated uncertainty; (2) energy management policies must consider future energy availability while making decisions to ensure that sufficient energy is available when there is no ambient energy. Prior approaches typically consider energy in the immediate future (e.g., 1 hour) and do not account for the uncertainty in future energy harvest. This article proposes novel machine learning and dynamic optimization-based approaches to handle the two challenges. Specifically, we first develop a novel set of features and use it in a low-power neural network architecture to predict future energy availability and uncertainty. The energy predictions and uncertainty are used in a dynamic optimization algorithm to optimally allocate the harvested energy. Experiments on solar energy data over 5 years from Golden, Colorado, show that the proposed energy prediction model achieves 3.4 J mean absolute error while having a coverage of 80%. Moreover, our energy management algorithm provides energy allocations that are within 2.5 J of an optimal Oracle with 2.65 mJ to 36.54 mJ of energy overhead.

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Cited By

Sixdenier PWildermann STeich J(2024)GRES: Guaranteed Remaining Energy Scheduling of Energy-harvesting Sensors by Quality Adaptation2024 13th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO62516.2024.10577838(1-5)Online publication date: 11-Jun-2024
https://doi.org/10.1109/MECO62516.2024.10577838
Capogrosso LCunico FCheng DFummi FCristani M(2024)A Machine Learning-Oriented Survey on Tiny Machine LearningIEEE Access10.1109/ACCESS.2024.336534912(23406-23426)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3365349

Index Terms

Uncertainty-aware Energy Harvest Prediction and Management for IoT Devices
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
2. Hardware
  1. Power and energy
    1. Energy generation and storage
      1. Renewable energy

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 28, Issue 5

September 2023

475 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3623508

Editor:
X. Sharon Hu
University of Notre Dame, USA

Issue’s Table of Contents

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(s).

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 09 September 2023

Online AM: 29 June 2023

Accepted: 04 June 2023

Revised: 19 May 2023

Received: 24 January 2023

Published in TODAES Volume 28, Issue 5

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Cited By

Sixdenier PWildermann STeich J(2024)GRES: Guaranteed Remaining Energy Scheduling of Energy-harvesting Sensors by Quality Adaptation2024 13th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO62516.2024.10577838(1-5)Online publication date: 11-Jun-2024
https://doi.org/10.1109/MECO62516.2024.10577838
Capogrosso LCunico FCheng DFummi FCristani M(2024)A Machine Learning-Oriented Survey on Tiny Machine LearningIEEE Access10.1109/ACCESS.2024.336534912(23406-23426)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3365349

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