Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting
Industrial Internet of Things (IIoT) applications involve real-time monitoring, detection, and data analysis. However, the intermittent activity of IIoT devices and limited battery capacity pose critical challenges. This paper addresses these interconnected issues, focusing on extending the battery...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11054051/ |
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| author | David E. Ruiz-Guirola Onel L. A. Lopez Samuel Montejo-Sanchez |
| author_facet | David E. Ruiz-Guirola Onel L. A. Lopez Samuel Montejo-Sanchez |
| author_sort | David E. Ruiz-Guirola |
| collection | DOAJ |
| description | Industrial Internet of Things (IIoT) applications involve real-time monitoring, detection, and data analysis. However, the intermittent activity of IIoT devices and limited battery capacity pose critical challenges. This paper addresses these interconnected issues, focusing on extending the battery life of IIoT devices sensing events/alarms by minimizing the number of unnecessary transmissions. We propose a threshold-based transmission-decision policy based on the sensing quality and the network spatial deployment. We optimize the transmission thresholds using several approaches such as successive convex approximation, block coordinate descent methods, Voronoi diagrams, explainable machine learning, algorithms based on natural selection and social behavior, and Q-learning. Through numerical evaluation, we demonstrate significant performance enhancements in low-power IIoT environments, with Q-learning performing the best, while the block coordinate descending method performs the worst. We compare the proposed methods to a benchmark that assigns the same transmission threshold to all devices. In low-density scenarios, all proposed methods outperform the benchmark, while in high-density scenarios, only Voronoi-(i), K-nearest neighbors, and Q-learning show better performance. Power consumption is reduced by up to 95% in low-density scenarios compared to the benchmark and by 63% in high-density scenarios. |
| format | Article |
| id | doaj-art-3e042c5279ba4ceda918fbe60294c95f |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-3e042c5279ba4ceda918fbe60294c95f2025-08-20T03:28:44ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0165490550810.1109/OJCOMS.2025.358386711054051Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event ReportingDavid E. Ruiz-Guirola0https://orcid.org/0000-0002-2096-8890Onel L. A. Lopez1https://orcid.org/0000-0003-1838-5183Samuel Montejo-Sanchez2https://orcid.org/0000-0003-1622-3180Centre for Wireless Communications, University of Oulu, Oulu, FinlandCentre for Wireless Communications, University of Oulu, Oulu, FinlandInstituto Universitario de Investigación y Desarrollo Tecnológico, Universidad Tecnológica Metropolitana, Santiago, ChileIndustrial Internet of Things (IIoT) applications involve real-time monitoring, detection, and data analysis. However, the intermittent activity of IIoT devices and limited battery capacity pose critical challenges. This paper addresses these interconnected issues, focusing on extending the battery life of IIoT devices sensing events/alarms by minimizing the number of unnecessary transmissions. We propose a threshold-based transmission-decision policy based on the sensing quality and the network spatial deployment. We optimize the transmission thresholds using several approaches such as successive convex approximation, block coordinate descent methods, Voronoi diagrams, explainable machine learning, algorithms based on natural selection and social behavior, and Q-learning. Through numerical evaluation, we demonstrate significant performance enhancements in low-power IIoT environments, with Q-learning performing the best, while the block coordinate descending method performs the worst. We compare the proposed methods to a benchmark that assigns the same transmission threshold to all devices. In low-density scenarios, all proposed methods outperform the benchmark, while in high-density scenarios, only Voronoi-(i), K-nearest neighbors, and Q-learning show better performance. Power consumption is reduced by up to 95% in low-density scenarios compared to the benchmark and by 63% in high-density scenarios.https://ieeexplore.ieee.org/document/11054051/Alarm scenarioindustrial Internet of Thingsmachine learningspatial correlationtransmission threshold |
| spellingShingle | David E. Ruiz-Guirola Onel L. A. Lopez Samuel Montejo-Sanchez Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting IEEE Open Journal of the Communications Society Alarm scenario industrial Internet of Things machine learning spatial correlation transmission threshold |
| title | Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting |
| title_full | Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting |
| title_fullStr | Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting |
| title_full_unstemmed | Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting |
| title_short | Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting |
| title_sort | configuring transmission thresholds in iiot alarm scenarios for energy efficient event reporting |
| topic | Alarm scenario industrial Internet of Things machine learning spatial correlation transmission threshold |
| url | https://ieeexplore.ieee.org/document/11054051/ |
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