Auto-Encoder Learning-Based UAV Communications for Livestock Management
The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/6/10/276 |
| _version_ | 1797473693707796480 |
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| author | Mohammed A. Alanezi Abdullahi Mohammad Yusuf A. Sha’aban Houssem R. E. H. Bouchekara Mohammad S. Shahriar |
| author_facet | Mohammed A. Alanezi Abdullahi Mohammad Yusuf A. Sha’aban Houssem R. E. H. Bouchekara Mohammad S. Shahriar |
| author_sort | Mohammed A. Alanezi |
| collection | DOAJ |
| description | The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge. |
| first_indexed | 2024-03-09T20:20:05Z |
| format | Article |
| id | doaj.art-15877e30d1934e45ad3eeb16a3106145 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T20:20:05Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-15877e30d1934e45ad3eeb16a31061452023-11-23T23:49:42ZengMDPI AGDrones2504-446X2022-09-0161027610.3390/drones6100276Auto-Encoder Learning-Based UAV Communications for Livestock ManagementMohammed A. Alanezi0Abdullahi Mohammad1Yusuf A. Sha’aban2Houssem R. E. H. Bouchekara3Mohammad S. Shahriar4Department of Computer Science and Engineering Technology, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi ArabiaDepartment of Computer Engineering, Ahmadu Bello University, Zaria 810001, NigeriaDepartment of Computer Engineering, Ahmadu Bello University, Zaria 810001, NigeriaDepartment of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi ArabiaDepartment of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi ArabiaThe advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge.https://www.mdpi.com/2504-446X/6/10/276unmanned aerial vehicleconvolutional auto-encoderlivestock farmingdeep neural networks |
| spellingShingle | Mohammed A. Alanezi Abdullahi Mohammad Yusuf A. Sha’aban Houssem R. E. H. Bouchekara Mohammad S. Shahriar Auto-Encoder Learning-Based UAV Communications for Livestock Management Drones unmanned aerial vehicle convolutional auto-encoder livestock farming deep neural networks |
| title | Auto-Encoder Learning-Based UAV Communications for Livestock Management |
| title_full | Auto-Encoder Learning-Based UAV Communications for Livestock Management |
| title_fullStr | Auto-Encoder Learning-Based UAV Communications for Livestock Management |
| title_full_unstemmed | Auto-Encoder Learning-Based UAV Communications for Livestock Management |
| title_short | Auto-Encoder Learning-Based UAV Communications for Livestock Management |
| title_sort | auto encoder learning based uav communications for livestock management |
| topic | unmanned aerial vehicle convolutional auto-encoder livestock farming deep neural networks |
| url | https://www.mdpi.com/2504-446X/6/10/276 |
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