A Context-Aware Blockchain-Based Crowdsourcing Framework: Open Challenges and Opportunities

Abstract

Crowdsourcing is a rapidly growing paradigm that commercial platforms such as Amazon MTurk and UpWork are adopting for allocating tasks to workers. Such frameworks typically employ a centralized infrastructure to implement required mechanisms such as task allocation, submission evaluation, and payment computation. However, centralized deployment comes with unresolved challenges in terms of trust, reliability, and transparency. Blockchain technology has been embraced for the deployment of crowdsourcing frameworks to enable trusted and autonomous execution. Each of the existing Blockchain-based crowdsourcing/ crowdsensing framework targets a specific application context due to the constraint capabilities of Blockchain. In this paper, we propose a context-aware Blockchain-based crowdsourcing framework where the context is defined by task requirements and workers' availability. The proposed framework is developed upon the review of existing works integrating Blockchain and crowdsourcing where the challenges and future directions are identified. The proposed framework has two classes of components: 1) core components implementing the basic framework functionalities, and 2) advanced components which are context and data managers that help improve the framework performance. The Advanced Context Manager is designed to monitor the current context and select the mechanisms to run for the core components accordingly. The core components are implemented as smart contracts on Blockchain for autonomous and trusted execution, while the advanced components are implemented spanning Blockchain and the cloud for flexibility and scalability. A case study demonstrating the performance of context-aware task allocation algorithms is presented. It shows how capturing the current system context can help achieve better overall performance based on the objective of the sensing application under consideration.