Seminar: Delay Tolerant Networking Based On Human Mobility Prediction Using Mobile Phone Data

Ali Farrokhtala
Ph.D. Oral Comprehensive
Supervisory Committee: Drs. Yuanzhu Chen, Minglun Gong and Ting Hu

Delay Tolerant Networking Based On Human Mobility Prediction Using Mobile Phone Data

Department of Computer Science
Tuesday, December 11, 2018, 11:00a.m., Room EN 2022


Abstract

Since past decades, human dynamics or the research of understanding human behavior and its effect on society, economic, and technological modern life, has become the focus of many studies. Fundamental breakthroughs can happen when we are surrounded by many technosocial systems, which are both driven by individual human actions and would shape our societies structure. Current studies assume that human mobility, as one part of human dynamics limited to whether the individual level or a group of people, is not randomly distributed in time and space. Furthermore, the recent evidence shows these activities follow a burstiness property and cyclic spatiotemporal patterns. Comprehensive knowledge about human mobility benefits various sciences and applications such as urban planning, economic forecasting, resource management, computer networking, and mobile communication. In this research, we reveal some of the spatiotemporal patterns of human mobility byusing graph theory and temporal complex network analysis on mobile phone data. We show how we can classify mobile phone users based on their smartphones Wi-Fi records into different groups such as student, faculty members or staffs. Wi-Fi logs hold the information of access points to which each user connects (or observes in periodic scans), and therefore they can be interpreted as the visited locations (geolocation information) and the in-person contacts (ties and social interaction information). We aim to employ the perceiving movement patterns into a mobility model and further a prediction system, which can potentially improve communication of nodes in the mobile wireless networks.

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