A Blockchain-Based Secure and Fair Online Incentive Mechanism for Crowdsensed Data Trading

Xiao Fang, Hui Cai, Biyun Sheng, Juan Li, Jian Zhou, Haiping Huang, Mang Ye, Fu Xiao

Abstract: With the development of blockchain technology, Blockchain-based Crowdsensed Data Trading (BCDT) has emerged as an attractive data exchange paradigm. Although it addresses security issues in data transactions, most recent research primarily focuses on offline scenarios, overlooking the critical importance of enabling real-time online data trading, where it suffers from dynamic worker participation and potential malicious attacks. In this paper, we propose a Blockchain-based Secure and Fair Online Incentive Mechanism (BSFOIM), which primarily incorporates a smart contract called BSFOIMToken, designed to function in online scenarios. In particular, we first introduce a multi-stage auction combined with a time discount factor in BSFOIM to quantify the contribution of workers in completing sensing tasks. Meanwhile, to ensure sensing data quality and worker selection fairness, we propose a Fairness-based Truth Discovery Mechanism (FTDM) with two core modules: a fine-grained reputation system to identify reliable workers and filter out malicious ones, and an upper confidence bound algorithm to optimize worker selection and avoid local optima. Finally, we implement these functions in BSFOIMToken and deploy a prototype on the Ethereum blockchain, demonstrating its practicality and robust performance. Rigorous theoretical and comprehensive experimental tests have proven their adherence to truthfulness, budget feasibility and individual rationality.

External IDs:doi:10.1109/tifs.2025.3607256