Recent advances in micro-electro-mechanical systems (MEMS) technology have enabled the development of low-cost, low-power, multi-functional and small-size sensors to be embedded in mobile devices, such as smartphones, wearable devices and in-vehicle sensors. Many mobile devices come with Internet connectivity and embedded sensors (e.g., accelerometers, gyroscope, microphone, video camera, GPS, and speed sensors), thereby turning themselves into well-functioned sensor boxes to probe personal activities and environmental phenomena in the vicinity. Consequently, a new sensing paradigm named Mobile Crowd Sensing (MCS) comes into being to harness the potential of the widespread mobile sensors and describe the dynamic patterns of the physical world across a wide variety of application domains. These sensing capable mobile devices, which consist of sensing, data processing, and communicating components, represent a significant improvement over traditional sensors networks. Most existing sensor networks still require overwhelming expenditure and professional personnel for both deployment and long-term maintenance, which are unaffordable for individuals and small companies. As a result, many areas of interest are out of sensing coverage and the collected data are insufficient for various application requirements. Worse still, the underutilization of current sensor networks and sensor-equipped mobile devices are wasteful of existing investment, as the sensor motes are static and they stay in hibernation mode most of time. Comparatively, MCS offers several advantages over the traditional sensor network infrastructures. First, MCS is built on the already-existing mobile devices with broad network access (e.g., cellular base stations and wifi access points), which are globally widespread and ready to be used. The persuasiveness of smartphones on an unprecedented scale reduces the deployment cost to almost zero, while the traditional static sensor networks involve overwhelming deployment costs that cannot be afforded by individuals and small enterprises. Furthermore, the movement of mobile device carriers implicates high spatio-temporal coverage and increases the possibility of capturing unexpected events as compared to static sensor networks. Last but not the least, the human involvement could provide additional intelligence such as persuasive user feedback on the sensor data, thus promoting the quality of service and improving the user experience of sensing applications.