Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization
As unmanned aerial vehicle (UAV) technology expands into diverse applications, the demand for enhanced performance intensifies. Blockchain sharding technology offers promising avenues for improving data processing capabilities and security in drone networks. However, the inherent mobility of UAVs an...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7279 |
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| author | Kaiyin Lu Xinguang Zhang Tianbo Zhai Mengjie Zhou |
| author_facet | Kaiyin Lu Xinguang Zhang Tianbo Zhai Mengjie Zhou |
| author_sort | Kaiyin Lu |
| collection | DOAJ |
| description | As unmanned aerial vehicle (UAV) technology expands into diverse applications, the demand for enhanced performance intensifies. Blockchain sharding technology offers promising avenues for improving data processing capabilities and security in drone networks. However, the inherent mobility of UAVs and their dynamic operational environment pose significant challenges to conventional sharding techniques, often resulting in communication latencies and data synchronization delays that compromise efficiency. This study presents a novel blockchain-based adaptive sharding framework specifically designed for UAV ecosystems. Our research extends beyond improving data transmission rates to encompass an enhanced Asynchronous Advantage Actor–Critic algorithm, tailored to address long-term optimization objectives in aerial networks. The proposed optimizations focus on dual objectives: enhancing data security while concurrently accelerating processing speeds. By addressing the limitations of traditional approaches, this work aims to facilitate seamless communication and foster innovation in UAV networks. The adaptive sharding framework, coupled with the refined A3C algorithm, presents a comprehensive solution to the unique challenges faced by mobile aerial systems in blockchain implementation. |
| format | Article |
| id | doaj-art-121a6a8b8f5c44e0ab4e831718d82305 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-121a6a8b8f5c44e0ab4e831718d823052025-08-20T01:53:57ZengMDPI AGSensors1424-82202024-11-012422727910.3390/s24227279Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain OptimizationKaiyin Lu0Xinguang Zhang1Tianbo Zhai2Mengjie Zhou3Department of Computer Science, School of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaThe Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USADepartment of Computer Science, School of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaDepartment of Computer Science, University of Bristol, Bristol BS8 1QU, UKAs unmanned aerial vehicle (UAV) technology expands into diverse applications, the demand for enhanced performance intensifies. Blockchain sharding technology offers promising avenues for improving data processing capabilities and security in drone networks. However, the inherent mobility of UAVs and their dynamic operational environment pose significant challenges to conventional sharding techniques, often resulting in communication latencies and data synchronization delays that compromise efficiency. This study presents a novel blockchain-based adaptive sharding framework specifically designed for UAV ecosystems. Our research extends beyond improving data transmission rates to encompass an enhanced Asynchronous Advantage Actor–Critic algorithm, tailored to address long-term optimization objectives in aerial networks. The proposed optimizations focus on dual objectives: enhancing data security while concurrently accelerating processing speeds. By addressing the limitations of traditional approaches, this work aims to facilitate seamless communication and foster innovation in UAV networks. The adaptive sharding framework, coupled with the refined A3C algorithm, presents a comprehensive solution to the unique challenges faced by mobile aerial systems in blockchain implementation.https://www.mdpi.com/1424-8220/24/22/7279UAV networkblockchain technologyadaptive shardingA3C algorithm |
| spellingShingle | Kaiyin Lu Xinguang Zhang Tianbo Zhai Mengjie Zhou Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization Sensors UAV network blockchain technology adaptive sharding A3C algorithm |
| title | Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization |
| title_full | Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization |
| title_fullStr | Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization |
| title_full_unstemmed | Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization |
| title_short | Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization |
| title_sort | adaptive sharding for uav networks a deep reinforcement learning approach to blockchain optimization |
| topic | UAV network blockchain technology adaptive sharding A3C algorithm |
| url | https://www.mdpi.com/1424-8220/24/22/7279 |
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