ABSTRACT

Recent years have witnessed the rise of Internet of Things (IoT), a newly emergent networking paradigm that connects humans and the physical world through ubiquitous sensing, computing, and communicating devices. With the ultimate goal of building reliable, robust, and secure IoT systems that are usually composed of multitudes of unreliable wireless devices - sometimes even carried by malicious users - this project develops TRIP, a 3-in-1 integrated framework of TRuth discovery, Incentive, and Privacy preserving mechanisms for IoT systems. This framework consists of 1) a truth discovery mechanism that can distill true information from the deluge of sensory data generated by the ubiquitous IoT devices, 2) a security and privacy mechanism that can not only protect user privacy but also defend against malicious attack, and 3) an incentive mechanism that can select reliable participants in order to maximize the quality of collected information. The successful completion of this project, which for the first time systematically investigates an integrated design of IoT systems, will facilitate not only a whole spectrum of applications that have significant natural and societal impact, but also various curriculum development and outreach activities that can benefit a large group of students, especially the female and minority students.

In order to realize the proposed TRIP system, this project addresses a series of important research challenges. First, to infer truth information from the noisy and sparse sensory data collected by human-carried IoT devices, this project delivers a novel truth discovery method that can capture the variety in the reliability of different users as well as the correlations among the objects. Second, this project investigates not only privacy preserving mechanisms that allow the server to conduct truth discovery operations without knowing the genuine values of user observations, but also security solutions that defend against malicious users who try to inject false information for the purpose of sabotage or financial rewards. Third, an effective incentive mechanism is developed to motivate reliable users to contribute to the IoT tasks. Finally, the above mechanisms are seamlessly integrated into networked IoT systems in a distributed and parallel manner, and evaluated on real testbeds in order to validate the effectiveness, efficiency, and the practicality of the proposed research.

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