Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular… Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.

The term affective computing was coined twenty years ago to refer to computers human-like capabilities to detect and recognize user’s emotions. Mobile sensing systems can be used to sense the emotional state of one or more users and let a third-party can use this information to produce changes in the user’s emotional state, or analyze hundreds of thousands of pictures, gestures, speechs and so on of people and train recognition systems for affective computing applications . For example teachers or e-learning systems as third-party systems can react appropriately maintaining motivation for their students according their emotional states using augmented reality techniques or changing the multimedia resources used in the lectures. Despite the direct benefits of knowing the emotional states, people in general is opposed to a system captures their emotions with smart phones equipped with built-in or external sensors such as image sensor to capture images and record videos, or a pressure sensor to detect the force or rhythm of a finger or stylus pen strokes within the display area. For that reason, currently privacy is one of the important barrier that limits the social acceptance of mobile sensing systems to do affective computing. In this paper we focus on mobile sensing systems to do affective computing preserving the user’s privacy to motivate the users to be sensed.