# CrowdOS: A Ubiquitous Operating System for Crowdsourcing and Mobile Crowd Sensing

arXiv: https://arxiv.org/abs/1909.00805 · HTML: https://mlsanity.com/paper/1909.00805/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing](https://mlsanity.com/paper/2410.15449/) (2024)
- [A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd](https://mlsanity.com/paper/2301.08563/) (2023)

## Abstract (arXiv)

With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms (CAP) have appeared. In the mean time, CAP-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However, CAP urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types, thus requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including Task Resolution and Assignment Framework (TRAF), Integrated Resource Management (IRM), and Task Result quality Optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further reveals TRO brings enormous improvement in efficiency and a reduction in energy consumption.