INCENTIVE MECHANISM DESIGN FOR PARTICIPATORY SENSING by Jingyi Sun Thesis Supervisor: Dr. Fen Hou Electrical and Computer Engineering Participatory Sensing (PS) is a novel paradigm to collect information, where individuals or communities use their mobile devices to sense and report the information about the surrounding environment such as temperature, noise level, etc. It can leverage a large amount of smartphone users to collect and analyze the sensing data. The applications cover various areas such as traffic control and management, environmental monitoring, etc. A participatory sensing system usually consists of three parts: service provider (SP), smartphone user (SU), and the platform. SP works as the task promulgator, SU works as the task executor, and the Platform handles the sensing task allocation procedure. Incentive mechanism plays a key role in stimulating both SPs and SUs to take part in the participatory sensing system. Most of existing works on the incentive mechanism design do not consider the quality of sensing data and the social relationship of SUs, which may degrade the performance of the participatory sensing system. For instance, the SUs may submit the erroneous or unreliable data, and low-quality data will impact the accuracy of data analysis result and degrade the service of SP. On the other hand, allocating sensing tasks to SUs who are friends can boost the participation level in PS system and SUs can achieve more happiness, since they can share their sensing activities and information via online social networks (e.g., WeChat, Facebook, LinkedIn etc.). In this research, we address these two issues respectively. In the first one, we consider a PS system consisting of one SP, multiple SUs and one platform. We take the quality of sensing data into consideration and design a Reputation-aware Incentive Mechanism (RAIM) which can maximize the weighted social welfare of the whole system, guarantee the truthfulness and individual rationality. Simulation results show the better performance of RAIM compared with three other counterparts in terms of weighted social welfare and average reputation. Especially, the RAIM can improve the weighted social welfare by 8.65% and 48.16% compared with Trustworthy Sensing for Crowd Management (TSCM) and random selection, respectively, when the number of smartphone users is 18. In the second network scenario, we consider a PS system with multiple SPs, multiple SUs and one platform. We take the social relationship of SUs into consideration and design a Social-aware Incentive Mechanism (SAIM) which can achieve high system performance and satisfy the properties of individual rationality, budget balance, the completely truthful for SPs and partially truthful for SUs. Simulation results show the better performance of SAIM compared with two other counterparts in terms of social utility and social effect. Especially, the proposed SAIM can improve the social utility by 12% and 16% comparted with McAfee and random selection, respectively, when the number of smartphone users is 100.