A sharding blockchain-based UAV system for search and rescue missions

Abstract

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle (UAV) search and rescue (SAR) systems. By breaking up the blockchain network into smaller partitions called shards that run independently and in parallel, sharding-based UAV systems can support a large number of search and rescue UAVs with improved scalability, thereby enhancing the rescue potential. However, the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR systems. Adaptability refers to making adjustments to the blockchain towards real-time surrounding situations, while interoperability refers to making cross-shard interactions at the mission level. To address the above challenges, we propose a blockchain UAV system for SAR missions based on dynamic sharding mechanism. Apart from the benefits in scalability brought by sharding, our system improves adaptability by dynamically creating configurable and mission-exclusive shards, and improves interoperability by supporting calls between smart contracts that are deployed on different shards. We implement a prototype of our system based on Quorum, give an analysis of the improved adaptability and interoperability, and conduct experiments to evaluate the performance. The results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

Keywords

blockchain / sharding / unmanned aerial vehicle / search and rescue / blockchain interoperability

Xihan Zhang received his BE degree in computing science from Beijing University of Post and Telecommunication, China in 2022. He is currently working toward a MS degree in Peking University, China. His main research interests include blockchain, software engineering and privacy

Jiashuo Zhang received the BE degree in computer science from Peking University, China in 2021. He is currently a PhD student in the School of Computer Science, Peking University, China. His research interests include blockchain and software engineering

Jianbo Gao received the BE and PhD degrees in computer science from Peking University, China in 2016 and 2021, respectively. He is currently working as a postdoctoral researcher with the School of Computer Science, Peking University, China. His research interests include blockchain, software engineering, and cyber security

Libin Xia received his BE degree in Information Security from Shanghai Jiao Tong University, China in 2022. He is currently working toward a MS degree in Peking University, China. His main research interests include blockchain, smart contracts and privacy

Zhi Guan received the PhD degree in computer science from Peking University, China in 2009, He has been a faculty member of the MoE (Ministry of Education) Key Laboratory of Network and Software Security Assurance, Peking University and National Engineering Research Center of Software Engineering of Peking University since 2009. He is an associate professor and his current research interests include cryptography and blockchain

Hao Hu received the PhD degree in computer science from Nanjing University, China in 2009. He joined the State Key Laboratory for Novel Software Technology and the Institute of Computer Software at Nanjing University, China in 2000. He is an associate professor and his current research interests include blockchain, swarm intelligence and Internetware

Zhong Chen graduated and received the PhD degree from Computer Science and Technology Department of Peking University, China in 1989, and became a faculty member of Peking University, China. He became a full professor in 1995. He is a professor of the School of Computer Science with Peking University, and the director of Metaverse Technology Institite, director of Network and Information Security Laboratory of Peking University, China. His current research interests include blockchain, domain-specific software engineering, network and information security

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Acknowledgements

This work was supported by the National Key R&D Program of China (2022YFB2703200) and the National Natural Science Foundation of China (Grant Nos. 62202011, 62172010).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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