CrowdKit: A Generic Programming Framework for Mobile Crowdsensing Applications

Mobile Crowdsensing (MCS) has become a popular sensing paradigm, where a number of participants use their mobile devices to collectively share and extract information related to a certain common interest. In this trend, many typical applications, such as environmental monitoring, intelligent transportation, and public safety, are emerging in our daily lives, and the need to quickly develop various new applications is becoming more urgent. However, existing programming frameworks for MCS applications either target specific scenarios that lack extensibility or require considerable development effort and expertise, hindering innovation in this direction. In order to reduce the burden of developing new MCS applications, we devise a developer-oriented generic programming framework, namely CrowdKit. It abstracts the common and fundamental data models and functions of MCS applications and makes them reusable. Meanwhile, it follows the principles of modular design, visual development, and automatic code generation to further bring extensibility and drastically reduce the difficulty and time cost of developers. Moreover, its algorithm modules can accommodate various advanced MCS algorithms, thus narrowing the gap between theory and practice. We implement and release a full-fledged version of CrowdKit, and conduct comprehensive case study and user study to demonstrate its simplicity, generality, extensibility and high efficiency.