ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing

• Published in: Applied sciences Vol. 12; no. 13; p. 6732
• Main Authors: Kodjiku, Seth Larweh, Fang, Yili, Han, Tao, Asamoah, Kwame Omono, Aggrey, Esther Stacy E. B., Sey, Collins, Aidoo, Evans, Ejianya, Victor Nonso, Wang, Xun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Adaptability; Behavior; Blockchain; Crowdsourcing; decentralized; Digital currencies; Innovations; Learning curves; Network reliability; Privacy; Reinforcement; Reputation management; smart contract; Starters; Workers
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12136732

Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.