CrowdOS: A Ubiquitous Operating System for Crowdsourcing and Mobile Crowd Sensing
Yimeng Liu, Zhiwen Yu, Bin Guo, Qi Han, Jiangbin Su, Jiahao Liao
Abstract
With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms ( <inline-formula><tex-math notation="LaTeX">$\mathbb {CAP}$</tex-math></inline-formula> ) have appeared. In the mean time, <inline-formula><tex-math notation="LaTeX">$\mathbb {CAP}$</tex-math></inline-formula> -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, <inline-formula><tex-math notation="LaTeX">$\mathbb {CAP}$</tex-math></inline-formula> 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 ( <i>TRAF</i> ), integrated resource management ( <i>IRM</i> ), and task result quality optimization ( <i>TRO</i> ). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further reveals <i>TRO</i> brings enormous improvement in efficiency and a reduction in energy consumption.