A Novel Mathematical Framework for Similarity-based Opportunistic Social Networks |
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| Affiliation: | 1. Department of Computer Science, University of California, Santa Barbara, USA;2. Electronics and Communications Engineering Dept., Faculty of Engineering, Cairo University, Giza 12613, Egypt;3. Department of Computer Science, University College Cork, Ireland;4. Computer Science and Engineering Dept., The American University in Cairo, AUC Avenue, New Cairo 11835, Egypt;5. Computer and Information Science and Engineering Department, University of Florida, Gainesville, USA;1. College of Computer Science and Engineering, University of Electronic Science and Technology of China, China;2. College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK;3. Department of Computing and Mathematics, University of Derby, Derby, UK;4. Department of Computer and Information Sciences and Digital Technologies, Northumbria University, Newcastle upon Tyne, UK;1. Department of Computer Science, University of Illinois at Urbana–Champaign, Urbana, IL 61801, United States;2. Department of Computer Science, University of Notre Dame, Notre Dame, IN 46556, United States;3. Networked Sensing & Fusion Branch, US Army Research Laboratory, Adelphi, MD 20783, United States;4. IBM Research, Yorktown Heights, NY, United States;1. Ibaraki University, 4-12-1 Nakanarusawa-cho, Hitachi-shi, Ibaraki, Japan;2. National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, Japan;1. Department of Electrical Engineering, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Xitun Dist., Taichung 40704, Taiwan;2. Department of Computer Science, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Xitun Dist., Taichung 40704, Taiwan;1. Department of Electrical & Information Engineering, Fuzhou University, Fuzhou, China;2. Department of Communication Engineering, Xiamen University, Xiamen, Fujian, China;3. Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan |
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| Abstract: | In this paper we study social networks as an enabling technology for new applications and services leveraging, largely unutilized, opportunistic mobile encounters. More specifically, we quantify mobile user similarity and introduce a novel mathematical framework, grounded in information theory, to characterize fundamental limits and quantify the performance of sample knowledge sharing strategies. First, we introduce generalized, non-temporal and temporal profile structures, beyond geographic location, as a probability mass function. Second, we examine classic and information-theoretic similarity metrics using data in the public domain. A noticeable finding is that temporal metrics give lower similarity indices on the average (i.e., conservative) compared to non-temporal metrics, due to leveraging the wealth of information in the temporal dimension. Third, we introduce a novel mathematical framework that establishes fundamental limits for knowledge sharing among similar opportunistic users. Finally, we show numerical results quantifying the cumulative knowledge gain over time and its upper bound, the knowledge gain limit, using public smartphone data for the user behavior and mobility traces, in the case of fixed as well as mobile scenarios. The presented results provide valuable insights highlighting the key role of the introduced information-theoretic framework in motivating future research along this ripe research direction, studying diverse scenarios as well as novel knowledge sharing strategies. |
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| Keywords: | Social networks Opportunistic Profiles Similarity Modeling User traces Numerical results |
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