Mobile encounters: pattern analysis and profile embedding for mobile social networking testbeds
Ahmed Helmy, Sungwook Moon · 2011
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 mimic human encounter patterns. We experiment with various types of decision modes and compare the results to random mobility and real-world networking trace. The result shows that our proposed approach provides the range of knobs for adjusting parameters to capture power-law distribution of group sizes, encounter ratio with group members and periodical encounter patterns that are close to real-world networking trace while far outperforming random mobility. Finally, we propose a novel mobile networking testbed that blends the network of autonomous robots and participatory testing via personality profile. We implement a prototype mobile networking testbed with IRobot and PDAs. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html)