Empowering Volunteer Crowdsourcing Services: A Serverless-assisted, Skill and Willingness Aware Task Assignment Approach for Amicable Volunteer Involvement

TL;DR

This paper presents SWAM, a serverless framework that improves volunteer-task matching in crowdsourcing by considering skills and willingness, significantly enhancing efficiency, task completion, and reducing waiting times.

Contribution

Introduction of SWAM, a skill and willingness-aware algorithm with a serverless deployment framework, to improve volunteer-task matching in crowdsourcing platforms.

Findings

71% improvement in end-to-end latency efficiency

92% task completion ratio achieved

56% reduction in task waiting time

Abstract

Volunteer crowdsourcing (VCS) leverages citizen interaction to address challenges by utilizing individuals' knowledge and skills. Complex social tasks often require collaboration among volunteers with diverse skill sets, and their willingness to engage is crucial. Matching tasks with the most suitable volunteers remains a significant challenge. VCS platforms face unpredictable demands in terms of tasks and volunteer requests, complicating the prediction of resource requirements for the volunteer-to-task assignment process. To address these challenges, we introduce the Skill and Willingness-Aware Volunteer Matching (SWAM) algorithm, which allocates volunteers to tasks based on skills, willingness, and task requirements. We also developed a serverless framework to deploy SWAM. Our method outperforms conventional solutions, achieving a 71% improvement in end-to-end latency efficiency. We achieved a 92% task completion ratio and reduced task waiting time by 56%, with an overall utility gain 30% higher than state-of-the-art baseline methods. This framework contributes to generating effective volunteer and task matches, supporting grassroots community coordination and fostering citizen involvement, ultimately contributing to social good.