RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION
In recent years, the use of remote sensing technology has grown exponentially in various industries such as agriculture, forestry, and urban planning. Remote sensing data collection systems rely on a network of nodes to collect and transmit data. The transmission capacity of these node networks is a...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/25/2023/isprs-archives-XLVIII-M-1-2023-25-2023.pdf |
| _version_ | 1797842602111795200 |
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| author | L. Bai X. Liu M. Zhao Z. Wang Z. Wang G. Shi |
| author_facet | L. Bai X. Liu M. Zhao Z. Wang Z. Wang G. Shi |
| author_sort | L. Bai |
| collection | DOAJ |
| description | In recent years, the use of remote sensing technology has grown exponentially in various industries such as agriculture, forestry, and urban planning. Remote sensing data collection systems rely on a network of nodes to collect and transmit data. The transmission capacity of these node networks is a critical factor in the performance and efficiency of the entire system. However, accurately predicting the transmission capacity of a node network can be a challenging task. To carry out large scale open remote sensing data collection, it is necessary to predict the network transmission capacity of nodes in the face of the difference in the execution speed of each node for various tasks. It is necessary to predict the network transmission capacity of nodes. In this research, we propose a node network transmission capacity prediction model for large scale remote sensing data collection using a combination of Particle Swarm Optimization (PSO) and Backpropagation (BP) algorithms. The proposed PSO-BP model aims to accurately predict the transmission capacity of a node network in a remote sensing data collection system. The model is tested and evaluated using a large-scale dataset and the results show that the proposed model outperforms existing models in terms of prediction accuracy. This work contributes to the field of remote sensing data collection by providing a reliable and efficient method for predicting the transmission capacity of node networks. |
| first_indexed | 2024-04-09T16:50:38Z |
| format | Article |
| id | doaj.art-da539ba7069f4d608579ff4956357993 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-09T16:50:38Z |
| publishDate | 2023-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-da539ba7069f4d608579ff49563579932023-04-21T14:12:12ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-04-01XLVIII-M-1-2023253110.5194/isprs-archives-XLVIII-M-1-2023-25-2023RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTIONL. Bai0X. Liu1M. Zhao2Z. Wang3Z. Wang4G. Shi5School of Computing, Ulster University, Belfast BT15 1ED, UKSchool of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, ChinaSchool of Communication and Electronic Engineering, Qiqihaer University, Qiqihaer 161003, ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, ChinaBohai-Rim Energy Research Institute, Northeast Petroleum University, Qinhuangdao 066004, ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, ChinaIn recent years, the use of remote sensing technology has grown exponentially in various industries such as agriculture, forestry, and urban planning. Remote sensing data collection systems rely on a network of nodes to collect and transmit data. The transmission capacity of these node networks is a critical factor in the performance and efficiency of the entire system. However, accurately predicting the transmission capacity of a node network can be a challenging task. To carry out large scale open remote sensing data collection, it is necessary to predict the network transmission capacity of nodes in the face of the difference in the execution speed of each node for various tasks. It is necessary to predict the network transmission capacity of nodes. In this research, we propose a node network transmission capacity prediction model for large scale remote sensing data collection using a combination of Particle Swarm Optimization (PSO) and Backpropagation (BP) algorithms. The proposed PSO-BP model aims to accurately predict the transmission capacity of a node network in a remote sensing data collection system. The model is tested and evaluated using a large-scale dataset and the results show that the proposed model outperforms existing models in terms of prediction accuracy. This work contributes to the field of remote sensing data collection by providing a reliable and efficient method for predicting the transmission capacity of node networks.https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/25/2023/isprs-archives-XLVIII-M-1-2023-25-2023.pdf |
| spellingShingle | L. Bai X. Liu M. Zhao Z. Wang Z. Wang G. Shi RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION |
| title_full | RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION |
| title_fullStr | RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION |
| title_full_unstemmed | RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION |
| title_short | RESEARCH ON NODE NETWORK TRANSMISSION CAPACITY PREDICTION MODEL FOR LARGE SCALE REMOTE SENSING DATA COLLECTION |
| title_sort | research on node network transmission capacity prediction model for large scale remote sensing data collection |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/25/2023/isprs-archives-XLVIII-M-1-2023-25-2023.pdf |
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