Towards Efficient and Deposit-Free Blockchain-Based Spatial Crowdsourcing

Author:

Li Mingzhe1ORCID,Wang Wei2ORCID,Zhang Jin3ORCID

Affiliation:

1. Southern University of Science and Technology, Shenzhen, China, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong and Agency for Science, Technology and Research, Singapore Singapore

2. The Hong Kong University of Science and Technology, Hong Kong Hong Kong

3. Southern University of Science and Technology, Shenzhen, China and Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Shenzhen, China

Abstract

Spatial crowdsourcing leverages the widespread use of mobile devices to outsource tasks to a crowd of users based on their geographical location. Despite its growing popularity, current crowdsourcing systems often suffer from a lack of transparency, centralization, and other security issues. Blockchain technology has revolutionized this sector with its potential for decentralization, security, and transparency. However, existing blockchain-based crowdsourcing systems often overlook efficient task assignment mechanisms and expose users to potential losses due to the obligatory deposit payments to smart contracts, which might be vulnerable or untrustworthy. This article proposes EDF-Crowd, an E fficient and D eposit- F ree blockchain-based spatial crowdsoucing framework, to address these challenges. EDF-Crowd introduces an efficient, customizable task assignment mechanism based on smart contracts, operating periodically and batch-wise. We also design a fair compensation mechanism to compensate users for the extra overhead caused by invoking certain smart contracts. More importantly, we propose a series of linkage protocols. By linking users’ back-and-forth actions, EDF-Crowd can regulate user behavior without requiring users to deposit. The versatility of EDF-Crowd also allows its application to generic crowdsourcing systems with minimal modifications. We implement EDF-Crowd based on the EOS blockchain. Extensive evaluations show that EDF-Crowd achieves high task assignment efficiency and low cost.

Publisher

Association for Computing Machinery (ACM)

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