UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning
Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (IS...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/11/647 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850267488127811584 |
|---|---|
| author | Yaxi Liu Xulong Li Boxin He Meng Gu Wei Huangfu |
| author_facet | Yaxi Liu Xulong Li Boxin He Meng Gu Wei Huangfu |
| author_sort | Yaxi Liu |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter. |
| format | Article |
| id | doaj-art-1a5a011e20b049109366238baef4df92 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-1a5a011e20b049109366238baef4df922025-08-20T01:53:45ZengMDPI AGDrones2504-446X2024-11-0181164710.3390/drones8110647UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement LearningYaxi Liu0Xulong Li1Boxin He2Meng Gu3Wei Huangfu4Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaUnmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter.https://www.mdpi.com/2504-446X/8/11/647unmanned aerial vehicledata collectionuplink communicationsensingdeep reinforcement learning |
| spellingShingle | Yaxi Liu Xulong Li Boxin He Meng Gu Wei Huangfu UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning Drones unmanned aerial vehicle data collection uplink communication sensing deep reinforcement learning |
| title | UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning |
| title_full | UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning |
| title_fullStr | UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning |
| title_full_unstemmed | UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning |
| title_short | UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning |
| title_sort | uav enabled diverse data collection via integrated sensing and communication functions based on deep reinforcement learning |
| topic | unmanned aerial vehicle data collection uplink communication sensing deep reinforcement learning |
| url | https://www.mdpi.com/2504-446X/8/11/647 |
| work_keys_str_mv | AT yaxiliu uavenableddiversedatacollectionviaintegratedsensingandcommunicationfunctionsbasedondeepreinforcementlearning AT xulongli uavenableddiversedatacollectionviaintegratedsensingandcommunicationfunctionsbasedondeepreinforcementlearning AT boxinhe uavenableddiversedatacollectionviaintegratedsensingandcommunicationfunctionsbasedondeepreinforcementlearning AT menggu uavenableddiversedatacollectionviaintegratedsensingandcommunicationfunctionsbasedondeepreinforcementlearning AT weihuangfu uavenableddiversedatacollectionviaintegratedsensingandcommunicationfunctionsbasedondeepreinforcementlearning |