Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach
Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging,...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4377 |
| _version_ | 1797493299858112512 |
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| author | Xiucheng Wang Lianhao Fu Nan Cheng Ruijin Sun Tom Luan Wei Quan Khalid Aldubaikhy |
| author_facet | Xiucheng Wang Lianhao Fu Nan Cheng Ruijin Sun Tom Luan Wei Quan Khalid Aldubaikhy |
| author_sort | Xiucheng Wang |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods. |
| first_indexed | 2024-03-10T01:18:02Z |
| format | Article |
| id | doaj.art-6183fb4d1d7d486f8d39e88b102adb8c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:18:02Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6183fb4d1d7d486f8d39e88b102adb8c2023-11-23T14:05:38ZengMDPI AGRemote Sensing2072-42922022-09-011417437710.3390/rs14174377Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based ApproachXiucheng Wang0Lianhao Fu1Nan Cheng2Ruijin Sun3Tom Luan4Wei Quan5Khalid Aldubaikhy6School of Telecommunications Engineering, Xidian University, Xi’an 710071, ChinaSchool of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an 710071, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an 710071, ChinaSchool of Cyber Engineering, Xidian University, Xi’an 710071, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Electrical Engineering, Qassim University, Buraydah 52571, Saudi ArabiaUnmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods.https://www.mdpi.com/2072-4292/14/17/4377UAVIoT networkgraph neural networkscalabilityreinforcement learning |
| spellingShingle | Xiucheng Wang Lianhao Fu Nan Cheng Ruijin Sun Tom Luan Wei Quan Khalid Aldubaikhy Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach Remote Sensing UAV IoT network graph neural network scalability reinforcement learning |
| title | Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach |
| title_full | Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach |
| title_fullStr | Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach |
| title_full_unstemmed | Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach |
| title_short | Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach |
| title_sort | joint flying relay location and routing optimization for 6g uav iot networks a graph neural network based approach |
| topic | UAV IoT network graph neural network scalability reinforcement learning |
| url | https://www.mdpi.com/2072-4292/14/17/4377 |
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