With the fast increasing popularity of mobile smart devices, mobile crowd sensing has become a new paradigm of applications that enables the ubiquitous mobile devices with enhanced sensing capabilities, such as smartphones and wearable devices, to collect and to share local information towards a common goal. Most of the smart devices are equipped with a rich set of cheap and powerful sensors, for example, accelerometer, digital compass, GPS, microphone, and camera. ese sensors can be utilized to monitor mobile users’ surrounding environment and infer human activities and contexts. In recent years, a wide variety of applications have been developed to realize the potential of crowd sensing throughout everyday life, such as environmental monitoring, noise pollution assessment, road and trac condition monitoring, road-side parking statistics, and indoor localization. e data acquired through mobile crowd sensing exhibits a number of important characteristics, such as being large in scale (Volume), being fast generated (Velocity), being dierent in forms (Variety), and being uncertain in quality (Veracity). e 4Vs of crowd sensing data make it extremely interesting and challenging in designing participatory and opportunistic sensing technologies, human centric data management and analytics models, and novel visualization tools.