Cargando…
Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model
In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of s...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302984/ https://www.ncbi.nlm.nih.gov/pubmed/37420603 http://dx.doi.org/10.3390/s23125437 |
_version_ | 1785065171677347840 |
---|---|
author | Liu, Yang Dong, Chen Qin, Xiaoqi Xu, Xiaodong |
author_facet | Liu, Yang Dong, Chen Qin, Xiaoqi Xu, Xiaodong |
author_sort | Liu, Yang |
collection | PubMed |
description | In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized. |
format | Online Article Text |
id | pubmed-10302984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103029842023-06-29 Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model Liu, Yang Dong, Chen Qin, Xiaoqi Xu, Xiaodong Sensors (Basel) Article In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized. MDPI 2023-06-08 /pmc/articles/PMC10302984/ /pubmed/37420603 http://dx.doi.org/10.3390/s23125437 Text en © 2023 by the authors. 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 Liu, Yang Dong, Chen Qin, Xiaoqi Xu, Xiaodong Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
title | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
title_full | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
title_fullStr | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
title_full_unstemmed | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
title_short | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
title_sort | efficient sensor scheduling strategy based on spatio-temporal scope information model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302984/ https://www.ncbi.nlm.nih.gov/pubmed/37420603 http://dx.doi.org/10.3390/s23125437 |
work_keys_str_mv | AT liuyang efficientsensorschedulingstrategybasedonspatiotemporalscopeinformationmodel AT dongchen efficientsensorschedulingstrategybasedonspatiotemporalscopeinformationmodel AT qinxiaoqi efficientsensorschedulingstrategybasedonspatiotemporalscopeinformationmodel AT xuxiaodong efficientsensorschedulingstrategybasedonspatiotemporalscopeinformationmodel |