On quantifying the quality of information in social sensing

This thesis develops the fundamental theory and methodology for quantifying the Quality of Information (QoI) in social sensing. We refer social sensing to the sensing applications where humans play a critical role in the sensing or data collection process. Social sensing has emerged as a new paradigm for sensory data collection, which is motivated by the proliferation of mobile platforms equipped with a variety of sensors (e.g., GPS, camera, microphone, motion and etc.) in the possession of common individuals, networking capabilities that enable fast and convenient data sharing (e.g., WiFi and 4G) and large-scale dissemination of opportunities (Twitter, Flicker and etc.). A significant challenge in social sensing applications lies in ascertaining the correctness of collected data and the reliability of information sources. We call this challenge QoI quantification in social sensing. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable. The term, participant (or source) reliability is used to denote the probability that the participant reports correct observations. Reliability may be impaired because of poor used sensor quality, lack of sensor calibration, lack of (human) attention to the task, or even intent to deceive. Moreover, data collection is often open to a large population, where it is impossible to screen all participants beforehand. The likelihood that a participant’s measurements are correct is usually unknown a priori. Consequently, it is very challenging to ascertain the correctness of the collected data from unreliable sources with unknown reliability. Meanwhile, it is also challenging to ascertain the reliability of each information source without knowing whether their collected data are true or not. Therefore, the main questions posed in this thesis are: i) whether or not we can determine, in an optimal way, given only the measurements collected and without knowing the reliability of sources, which of the reported observations are true and which are not? ii) whether a source (participant) is reliable or not? iii) how to quantify the answers of the above questions? The thesis answered the above questions by applying the key insights from estimation theory and data fusion to come up with new theories to accurately quantify both the participant reliability and correctness