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The world of telecommunications has witnessed the growing popularity of mobile devices and its huge technological advancements and innovations (e.g. smartphones, smartwatches, tablets, music players among others). These devices have a series of built-in sensors that measure motion, orientation, and various environmental conditions (e.g. Global Positioning System, camera, microphone, compass, accelerometer, among others). In addition, these devices have continuous network connectivity. So these devices can be seen as a huge opportunity to carry out large-scale sensing of events in the physical world and have the ability of sharing the data obtained through the internet. This new kind of sensor application is known as Mobile crowd sensing (MCS) and it has been a research focus lately. The greatest potential of MCS is found on the versatility that the embedded resources of the mobile devices offer in the development of innumerable functionalities and its mobility model that is based on human behavior. On the other hand, there are issues that must be considered when a MCS-based network is developed. This work presents the analysis performed in order to define issues that are considered critical for the creation and development of an MCS network. From these definitions solutions are proposed that allow to create an implementation as close as possible to reality. A mobility model was developed for the Rio de Janeiro city based on graph theory, and assuming that daily activities of the people will define their movement pattern. Attracting and convincing users is another problem that has to be addressed. Two user incentive models are proposed. Both consider and model the decision of a user to participate in an MCS network based on the intrinsic and extrinsic motivations of the user. The idea is to comprise different levels of motivation for each user in order to demonstrate that the response of the participants to the incentives is not homogeneous. Thus, the first model is based on the consecutive answers of the users and the second model is based on game theory. The results obtained allowed us to prove that the proposed incentive models can satisfactorily estimate the type of user with which we are interacting and the amount of incentive that should be offered to each one of them, besides demonstrating the advantages of an incentive system that considers variable payments. The advantages of considering human mobility in this type of approach and how it affects the incentive models was also analyzed.