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Title: Understanding, modeling and using flow context
Author: Ocampo, Roel Maglente
ISNI:       0000 0001 3454 8996
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2007
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This thesis presents a concept called flow context, defined as any information that can be used to characterize the situation of a sequence of protocol data units, called a flow, within a network. Flow context is designed to enable the realization of context-aware networks: networks that can sense, process, disseminate and use context information in order to enable or trigger services, or modify and optimize their operation. The thesis discusses a conceptualization for flow context, and describes its characteristics. A semantic model for flows and flow context in the form of an OWL-DL ontology is developed. The various components of the flow context life cycle, consisting of the stages of sensing, processing, dissemination and use, arc described. To account for its multi-faceted nature, a multi-dimensional approach to sensing flow con text is adopted. Novel implementations for three exemplar sensors, including sensors for intrinsic flow context, node and device characteristics, and for device location, are pre sented. Mechanisms for locating flow context sensors and propagating context event notifications using a distributed hash table (DHT) are described and evaluated. Simulation results suggest that DHTs can provide decentralized and scalable solutions for flow context location and dissemination. In addition, a novel mode of context distribution called path-coupled flow context dissemination is described. An evaluation of semantic flow context processing using the RacerPro reasoner is presented. Various platform-specific reasoning modes are tested, including the use of queries, ABox modification, and incomplete reasoning. Finally, several application scenarios illustrating the potential uses of flow context in areas such as mobility and moving networks, quality of service, intelligent flow classification, network management, and other applications, are presented. Many of these scenarios are demonstrated through proof-of-concept implementations, which may be further evaluated and developed into full, working, and useful applications.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available