Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656804
Title: Spatial stochastic population models for the analysis of city-scale systems
Author: Günther, Marcel Christoph
ISNI:       0000 0004 5349 5895
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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Abstract:
Recent advances in technology have led to a surge in innovations in the area of spatially aware applications such as locally operating social networks, retail, advertising, local weather and traffic services. Such applications are often supported by large data-collection and dissemination processes, designed to work on large-scale, inexpensive, infrastructure-light wireless \adhoc networks. As a consequence, novel modelling techniques are required for the purpose of capacity planning and in order to build on-line prediction models based on large quantities of location-aware data. In this thesis we study the spatio-temporal evolution of population systems related to such city-scale challenges. In particular we focus on large-scale, spatial population processes that are not amenable to fluid-flow or mean-field approximation techniques because of locally or temporarily varying population sizes. Our main contributions are - Providing novel ways of incorporating space and mobility in large-scale spatial populations models. - Illustrating how, for a certain class of spatial population processes, the time-evolution of higher-order population moments can be obtained efficiently using hybrid-simulation analysis. - Presenting case studies for realistic spatial systems from different application areas to show that our modelling techniques are well-suited for the analysis of network and protocol performance of static and mobile \adhoc communication networks as well as for building fast on-line prediction models.
Supervisor: Bradley, Jeremy Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.656804  DOI: Not available
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