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ACKNOWLEDGEMENTS I take this opportunity to express my sincere thanks to Dr. Miguel Labrador, for giving me this wonderful opportunity of working on this project. I am also grateful to him for his extended support and guidance throughout the course of this work, and for making my study at USF a pleasant and exciting educational experience. My sincere thanks to Dr. Sarkar and Dr. Moreno, for being in my committee and for their valuable comments and suggestions.It takes more than words to express my thanks to my family for their constant motivation and support, without which this work would not have been possible. I thank all my friends for their continuous encouragement and support.ABSTRACT v CHAPTER 1 iv ABSTRACT Participatory Sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their cellular phones. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would be very difficult to assess and solve. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation and privacy concerns.Without adequate incentive and privacy guaranteeing mechanisms most users will not be willing to participate. This thesis concentrates on incentive mechanisms for user participation in PS system.Although several schemes have been proposed thus far, none has used location information and imposed budget and coverage constraints, which will make the scheme more realistic and efficient.A recurrent reverse auction incentive mechanism with a greedy algorithm that selects a representative subset of the users according to their location given a fixed budget is proposed. Compared to existing mechanisms, GIA (i.e., Greedy Incentive Algorithm) improves the area covered by more than 60 percent acquiring a more representative set of samples after every round, i.e., reduces the collection of unnecessary (redundant) data, while maintaining the same number of active users in the system and spending the same budget.v CHAPTER 1

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