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

TL;DR

The paper tackles the challenge of matching tasks to volunteers in dynamic volunteer crowdsourcing by incorporating both skills and willingness into the assignment process. It introduces the Skill and Willingness-Aware Matching (SWAM) algorithm, which assigns volunteers to tasks using utilities $U_{v,t}=b_t\times W(v,t)$ with willingness $W(v,t)=\frac{1}{1+\exp(-\omega(v,t))}$ and $\omega(v,t)=eff_v+\beta_v$, and it deploys SWAM within a serverless framework for scalable, low-latency operation. An AWS-based deployment (Lambda, S3, Step Functions) processes data in batches, updates the Skill-Task Mapper and Volunteer-Skill Mapper, and computes final allocations $\mu$ with reduced latency. Evaluations on Meetup data show substantial improvements: end-to-end latency improved by about 71%, task waiting time reduced by 56%, and a 92% task completion rate, with roughly a 30% gain in overall utility compared to baselines. These results demonstrate a practical, scalable approach to real-time volunteer-task matching that supports grassroots coordination and social good.

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.

Figures (5)

• Figure 1: Overview of proposed Serverless-assisted Framework for task assignment in Volunteer Crowdsourcing Paradigm
• Figure 2: Serverless Deployment Framework for Task Assignment Mechanism
• Figure 3: Arrival pattern of tasks and volunteers
• Figure 4: Illustration of total end-to-end latency and average task waiting time of SWAM in serverless and local settings.
• Figure 5: Illustration of task completion ratio and overall utility score of SWAM, i-VTM and OG in the local setting.

Theorems & Definitions (8)

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