MOBILE ENCOUNTERS: PATTERN ANALYSIS AND PROFILE EMBEDDING FOR MOBILE SOCIAL NETWORKING TESTBEDS By SUNGWOOK MOON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy MOBILE ENCOUNTERS: PATTERN ANALYSIS AND PROFILE EMBEDDING FOR MOBILE SOCIAL NETWORKING TESTBEDS By Sungwook Moon December 2011 Chair: Ahmed Helmy Major: Computer Engineering Study on human mobility is gaining increasing attention from the research community for use in mobile networks. To better understand the potential of mobile nodes as message relays, our study first investigates the encounter pattern of mobile devices. Specifically, we examine extensive network traces that reflect mobility of communication devices. We analyze the periodicity and consistency of encounter patterns by using power spectral analysis. Our result shows the presence of strong periodicity for rarely encountering mobile nodes and weak periodicity for frequently encountering nodes. In addition, our investigation on the encounter history shows that consistency depends on the encounter rate and length of history. With this understanding of human encounter patterns, we discuss profiling of mobile users based on their periodic properties in encounter pattern. In addition, we group mobile users based on encounter days and discover that the rank group size follows power-law distribution that we use in the assignments of communities for autonomous nodes. To enhance the mobile networks testing, we utilize our findings to effectively capture and embed personality of mobile users in simulation and testbed environment. We propose an encounter rule-based decision to mimic human encounter pattern, which is an important step toward efficient design of mobile social networking protocols and services. With the additions of group information and scheduler to the rule-based decision, we show that our approaches enable autonomous mobile nodes collectively