  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.
