Seminars - Abstract
Ahmed Helmy, Ph.D.
associate professor, Department of Computer and Information Science and Engineering
University of Florida
Title: Data-driven Analysis, Modeling and Design for Future Mobile Networking
(From Campus-wide to Planet-scale Mobility Modeling)
Abstract: The future of networking is in the mobile world. Future network services are expected to center around human activity and behavior. Wireless networks (including ad hoc (MANETs), sensor networks, vehicular networks (VANETs) and DTNs) are expected to grow significantly and accommodate higher levels of mobility and interaction. In such a highly dynamic environment, networks need to adapt efficiently (performance-wise) and gracefully (correctness and functionality-wise) to growth and dynamics in many dimensions, including behavioral and mobility patterns, on-line activity and load. Understanding and realistically modeling this multi-dimensional space is essential to the design and evaluation of efficient protocols and services of the future Internet.
This level of understanding to drive the modeling and protocol design shall be developed using data-driven paradigm. The design philosophy for the proposed paradigm is unique in that it begins by intensive analysis of measurements from the target contexts, which then drive the modeling, protocol and service design through a systematic framework, called TRACE. Components of TRACE include: 1. Tracing and monitoring of behavior, 2. Representing and Analyzing the data, 3. Characterizing behavioral profiles using data mining and clustering techniques, and finally 4. Employing the understanding and insight attained into developing realistic models of mobile user behavior, and designing efficient protocols and services for future mobile societies.
Tracing at a large scale represents the next frontier for sensor networks (sensing the mobile society; including humans and vehicles). Our latest progress in that field (MobiLib) shall be presented, along with data mining and machine learning tools to meaningfully analyze the data. Several challenges will be presented and novel use of clustering algorithms will be provided. Major contributions to modeling of human mobility (the time variant community model, TVC) will also be discussed.
In addition, a planet-scale framework for capturing vehicular mobility is presented. This effort is conducted in collaboration with T-labs, Berlin, and utilizes thousands of street and highway webcams around the world. With the use of scalable image processing techniques, time series of vehicular density are generated, analyzed and mined. Various analyses are conducted to develop accurate models for vehicular mobility and perform self-similarity studies.
Finally, insights developed through analysis, mining and modeling will be utilized to introduce and design a novel communication paradigm, called profile-cast, to support new classes of service for interest-aware routing and dissemination of information, queries and resource discovery, trust and participatory sensing (crowd sourcing) in future mobile networks. Unlike conventional - unicast, multicast or directory based - paradigms, the proposed paradigm infers user interest using implicit behavioral profiling via self-monitoring and mining techniques. In order to capture interest, a spatio-temporal representation is introduced to capture users behavioral-space. Users can then identify similarity of interest based on their position in such space.
The proposed profile-cast paradigm will act as enabler to new classes of service, ranging from mobile social networking, and navigation of mobile societies and spaces, to computational health care and education, among others. The ideas of similarity-based support groups will be specifically highlighted for potential applications in disease-self management, collaborative education, and emergency response.