This thesis deals with theoretical but also practical aspects of Mobile Crowdsensing (MCS). MCS is a paradigm which employs personal mobile devices of private individuals to collect data for various purposes. It is an innovative and efficient method for gathering data while being subject to numerous challenges considering the involvement of humans, mobility as well as variations and limitations of mobile devices. It is precisely the human factor and the distributiveness in MCS that makes solution approaches based on a game-theoretic methodology so well-suited to address problems as is shown in this thesis. We present innovations and advancements in MCS in the areas of energy efficiency, robustness as well as user-oriented transparent, privacy-aware and secure handling of data collection. In addition to theoretical results, we present two real-world MCS applications. We also contribute to research in the field of Wireless Sensor Networks (WSNs) as well as to the Rendezvous problem.