research-article

Energy-Accuracy Tradeoff for Efficient Noise Monitoring and Prediction in Working Environments

Authors:

Frank Alexander Kraemer,

Ida Marie Vestgøte BoschAuthors Info & Claims

IoT '19: Proceedings of the 9th International Conference on the Internet of Things

Article No.: 14, Pages 1 - 8

https://doi.org/10.1145/3365871.3365885

Published: 22 October 2019 Publication History

Abstract

We explore the tradeoff between energy consumption and measurement accuracy for noise monitoring and prediction based on continuously collected data by wireless, energy-constrained IoT nodes. This tradeoff can be controlled by the sampling interval between measurements and is of interest for energy-efficient operation, but most of ten ignored in the literature. We study the influence of the sampling intervals on the accuracy of various noise indicators and metrics. To provide a context for the tradeoff, we consider the use case of noise monitoring in working environments and present a learning algorithm to also predict sound indicators. The results indicate that a proper tradeoff between energy consumption and accuracy can save considerable energy, while only leading to acceptable or insignificant reductions in accuracy, depending on the specific use case. For instance, we show that a system for monitoring and prediction can perform well for users and only uses around 7% of the energy compared to full sampling.

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Cited By

Murad AKraemer FBach KTaylor G(2024)Uncertainty-aware autonomous sensing with deep reinforcement learningFuture Generation Computer Systems10.1016/j.future.2024.03.021156(242-253)Online publication date: Jul-2024
https://doi.org/10.1016/j.future.2024.03.021
Alawad F(2022)Adaptive Sampling for Efficient Acoustic Noise Monitoring: An Incremental Learning Approach2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00064(176-184)Online publication date: Aug-2022
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00064
Espe AAndersen SRossi PKraemer FMathisen G(2022)An Analysis of Design Parameters for Energy Management of Wireless Sensor Devices2022 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT48603.2022.10002831(1-8)Online publication date: 22-Aug-2022
https://doi.org/10.1109/ICIT48603.2022.10002831
Show More Cited By

Index Terms

Energy-Accuracy Tradeoff for Efficient Noise Monitoring and Prediction in Working Environments
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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cover image ACM Other conferences

IoT '19: Proceedings of the 9th International Conference on the Internet of Things

October 2019

263 pages

ISBN:9781450372077

DOI:10.1145/3365871

Copyright © 2019 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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Association for Computing Machinery

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Publication History

Published: 22 October 2019

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IoT 2019

IoT 2019: 9th International Conference on the Internet of Things

October 22 - 25, 2019

Bilbao, Spain

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IoT '19 Paper Acceptance Rate 28 of 84 submissions, 33%;

Overall Acceptance Rate 28 of 84 submissions, 33%

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Cited By

Murad AKraemer FBach KTaylor G(2024)Uncertainty-aware autonomous sensing with deep reinforcement learningFuture Generation Computer Systems10.1016/j.future.2024.03.021156(242-253)Online publication date: Jul-2024
https://doi.org/10.1016/j.future.2024.03.021
Alawad F(2022)Adaptive Sampling for Efficient Acoustic Noise Monitoring: An Incremental Learning Approach2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00064(176-184)Online publication date: Aug-2022
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00064
Espe AAndersen SRossi PKraemer FMathisen G(2022)An Analysis of Design Parameters for Energy Management of Wireless Sensor Devices2022 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT48603.2022.10002831(1-8)Online publication date: 22-Aug-2022
https://doi.org/10.1109/ICIT48603.2022.10002831
Liu YShu LHuo ZTsang KHancke G(2021)Collaborative Industrial Internet of Things for Noise Mapping: Prospects and Research OpportunitiesIEEE Industrial Electronics Magazine10.1109/MIE.2020.304016215:2(52-64)Online publication date: Jun-2021
https://doi.org/10.1109/MIE.2020.3040162
Murad AKraemer FBach KTaylor GDavidsson PLangheinrich M(2020)Information-driven adaptive sensing based on deep reinforcement learningProceedings of the 10th International Conference on the Internet of Things10.1145/3410992.3411001(1-8)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1145/3410992.3411001
Gómez-Carmona OCasado-Mansilla DKraemer FLópez-de-Ipiña DGarcía-Zubia J(2020)Exploring the computational cost of machine learning at the edge for human-centric Internet of ThingsFuture Generation Computer Systems10.1016/j.future.2020.06.013112(670-683)Online publication date: Nov-2020
https://doi.org/10.1016/j.future.2020.06.013

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