• Data Collection
• Internet of Things
• Mobile Crowd Sensing
• Opportunistic Sensing
• Optimization

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices such as smartphones, tablets and wearable devices. For being effective, MCS systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation efforts. In this thesis I propose a novel distributed and sustainable framework for gathering information in cloud-based opportunistic mobile crowd sensing systems. This framework minimizes the cost of both sensing and reporting while maximizing at the same time the utility of data collection, i.e. the quality of contributed information. The proposed framework is evaluated for data collection both analytically and through simulations performed in a real urban environment and with a large number of participants.