Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks
Abstract
:1. Introduction
2. Related Work
2.1. Data Dissemination
2.2. Data Coding
2.3. Graph Representation and Graph Neural Networks
- The cooperative data dissemination problem is described in a distributed manner. We use graph structures to represent ad hoc networks and design the data structures of nodes and edges.
- This work improves wireless transmission quality through data encoding. A wireless communication protocol is designed to avoid message collision and adopts the Signal to Interference Noise Ratio (SINR) to evaluate the communication quality.
- We propose a distributed cooperative data dissemination method based on GNN. The method can adapt to the dynamic topology and enhance network efficiency and stabilization. We train the policy with a reward function that enhances the efficiency of each transmission and reduces the required number of time slots.
3. System Model
3.1. Scene Description
3.2. Local Data Structure
3.3. Communication Model
3.4. Network Coding Scheme
4. Proposed Solution
4.1. The Local Policy with Aggregation Graph Neural Networks
4.1.1. Definition of Graph
4.1.2. Graph Network Block
4.1.3. The Encoder-Process–Decoder Architecture
4.2. Transmission–Response Protocol Design
Algorithm 1 Transmission–Response Protocol |
Require: 1: while do 2: 3: 4: 5: 6: 7: if then 8: 9: 10: end if 11: 12: 13: 14: 15: if then 16: 17: 18: end if 19: 20: end while |
4.3. Reinforcement Learning
Reward Function Design
5. Performance Evaluation
5.1. Simulation Setup
5.2. Baselines
5.3. Simulation Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Symbol | Definition |
the UAV set | |
the packet set | |
the number of packets in packet set | |
the m th packet | |
the packet vector of UAV i with element at time slot t, | |
a transmission link from UAV to UAV | |
the distance between UAV and at time slot t | |
the noise effect | |
the pass-loss exponent | |
the transmission power of UAV | |
the SINR threshold | |
the SINR value of the transmission link at time slot t | |
the packet set UAV has | |
the packet set UAV wants | |
the position of UAV at time slot t | |
the velocity of UAV at time slot t | |
the state of UAV known by UAV at time slot t | |
the time slot for the observation of the state of UAV j | |
the first relay on the way from UAV to UAV at time slot t | |
the time slot when occurs at time slot t | |
the packet vector of UAV observed by UAV at time slot t | |
graph | |
node | |
edge | |
the policy function | |
the n th node attributes | |
the l th edge attributes | |
the global attributes | |
the update function of node attributes | |
the update function of edge attributes | |
the aggregation function which aggregate the edge features to node | |
the receiver set of UAV at time slot t | |
the set of the receiver set decided by the policy for UAV at time slot t | |
X0 | the number of packets that all UAVs have in the initial state |
X | the total number of packets that all UAVs should have |
x | the number of packets received by UAVs in a single slot |
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Xing, N.; Zhang, Y.; Wang, Y.; Zhou, Y. Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks. Sensors 2024, 24, 887. https://doi.org/10.3390/s24030887
Xing N, Zhang Y, Wang Y, Zhou Y. Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks. Sensors. 2024; 24(3):887. https://doi.org/10.3390/s24030887
Chicago/Turabian StyleXing, Na, Ye Zhang, Yuehai Wang, and Yang Zhou. 2024. "Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks" Sensors 24, no. 3: 887. https://doi.org/10.3390/s24030887
APA StyleXing, N., Zhang, Y., Wang, Y., & Zhou, Y. (2024). Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks. Sensors, 24(3), 887. https://doi.org/10.3390/s24030887