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A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks
Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611001/ https://www.ncbi.nlm.nih.gov/pubmed/37896571 http://dx.doi.org/10.3390/s23208478 |
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author | Płaczek, Bartłomiej |
author_facet | Płaczek, Bartłomiej |
author_sort | Płaczek, Bartłomiej |
collection | PubMed |
description | Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods. |
format | Online Article Text |
id | pubmed-10611001 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106110012023-10-28 A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks Płaczek, Bartłomiej Sensors (Basel) Article Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods. MDPI 2023-10-15 /pmc/articles/PMC10611001/ /pubmed/37896571 http://dx.doi.org/10.3390/s23208478 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Płaczek, Bartłomiej A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks |
title | A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks |
title_full | A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks |
title_fullStr | A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks |
title_full_unstemmed | A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks |
title_short | A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks |
title_sort | multi-agent prediction method for data sampling and transmission reduction in internet of things sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611001/ https://www.ncbi.nlm.nih.gov/pubmed/37896571 http://dx.doi.org/10.3390/s23208478 |
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