An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks
Abstract With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cos...
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| Language: | English |
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SpringerOpen
2024-10-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00412-5 |
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| author | Zhifeng Ma Zhanjun Hao Zhenya Zhao |
| author_facet | Zhifeng Ma Zhanjun Hao Zhenya Zhao |
| author_sort | Zhifeng Ma |
| collection | DOAJ |
| description | Abstract With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field. |
| format | Article |
| id | doaj-art-b90eff1f95c949c6b021cb28feb79732 |
| institution | OA Journals |
| issn | 2520-8942 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-b90eff1f95c949c6b021cb28feb797322025-08-20T02:17:46ZengSpringerOpenEnergy Informatics2520-89422024-10-017111810.1186/s42162-024-00412-5An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networksZhifeng Ma0Zhanjun Hao1Zhenya Zhao2College of Computer Science and Engineering, Northwest Normal UniversityCollege of Computer Science and Engineering, Northwest Normal UniversitySchool of Traffic and Transportation, Lanzhou Jiaotong UniversityAbstract With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.https://doi.org/10.1186/s42162-024-00412-5Wireless sensor networkOil and gas pipeline networkEnergy-saving schedulingTransformer networkEnergy efficiency |
| spellingShingle | Zhifeng Ma Zhanjun Hao Zhenya Zhao An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks Energy Informatics Wireless sensor network Oil and gas pipeline network Energy-saving scheduling Transformer network Energy efficiency |
| title | An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks |
| title_full | An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks |
| title_fullStr | An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks |
| title_full_unstemmed | An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks |
| title_short | An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks |
| title_sort | investigation on energy saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks |
| topic | Wireless sensor network Oil and gas pipeline network Energy-saving scheduling Transformer network Energy efficiency |
| url | https://doi.org/10.1186/s42162-024-00412-5 |
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