On-chain behavior prediction Machine Learning model for blockchain-based crowdsourcing

Abstract

In this paper, we address the problem of behavior prediction for task allocation in blockchain-based crowdsourcing framework. Centralized crowdsourcing frameworks complement workers’ reputations with predicted behavior, through Machine Learning (ML) models, to improve the task allocation performance and maintain worker engagement. Existing blockchain-based crowdsourcing frameworks allocate tasks to workers using reputation solely, which neglects the impact of a task’s context on the worker’s behavior. Our contribution is an on-chain behavior prediction ML model for task allocation on top of a proposed blockchain-based crowdsourcing framework. The ML model, hosted on blockchain, reflects a worker’s unique behavior for a task given its context. The proposed ML model is: (1) trained off-chain since it has lower monetary cost compared to on-chain training, and (2) deployed on-chain as a smart contract to enable transparent predictions. The task allocation mechanism in the proposed blockchain-based crowdsourcing framework considers workers’ predicted behavior and a Quality of Information (QoI) metric that includes distance to the task, completion time, and workers’ reputation. The evaluation conducted confirms that the proposed task allocation mechanism, implemented using Solidity, outperforms the benchmark in terms of percentage of allocation, workers’ QoI, and reputation change.