FishboneChain: A Scalable and Liquidity-Guaranteed Crowdsourcing Platform Based on Multiple Child Chains
The integration of blockchain with crowdsourcing improves data security, reduces single-point failure risks, and enhances trust and traceability in the system. However, this integration also leads to scalability issues because of the decentralized consensus mechanism of blockchain. While multiple chain architecture can enhance system throughput and offer functionality extensions, they often require complex token locking and release mechanisms to prevent double-spending attacks. This leads to a significant portion of funds being locked, thereby reducing overall fund liquidity. In this paper, we propose FishboneChain, consisting of one main chain and multiple child chains. Specifically, we address the scalability issue by leveraging child chains to offload the crowdsourcing task result submission and verification transaction pressure from the main chain, while each child chain can deploy various crowdsourcing protocols to enhance the functionality. Furthermore, we design a fund management contract and periodic task settlement mechanism, which allows requesters to lock a single fund on the main chain to manage task status across multiple child chains, thereby reducing the locked fund percentage and improving fund liquidity. Our scheme satisfies the security requirements of a crowdsourcing platform, such as fund security, robustness, and double-spending resistance. Performance analysis shows our scheme can improve the system throughput to 100 times that of the main chain and maintain the locked fund percentage below 20%.
Authors
- Meiqi Li
- Kaiping Xue (ORCID: https://orcid.org/0000-0003-2095-7523)
- Qibin Sun (ORCID: https://orcid.org/0000-0002-6789-7460)
- Xinyi Luo
Institutions
- University of Science and Technology of China (CN)
Publication Details
- Journal
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1109/tdsc.2025.3587900
- Primary Topic
- Mobile Crowdsensing and Crowdsourcing
- Type
- article
- Field-Weighted Citation Impact
- 0.00