Superprocesses as models for information dissemination in the Future Internet

Future Internet will be composed by a tremendous number of potentially interconnected people and devices, offering a variety of services, applications and communication opportunities. In particular, short-range wireless communications, which are available on almost all portable devices, will enable the formation of the largest cloud of interconnected, smart computing devices mankind has ever dreamed about: the Proximate Internet. In this paper, we consider superprocesses, more specifically super Brownian motion, as a suitable mathematical model to analyse a basic problem of information dissemination arising in the context of Proximate Internet. The proposed model provides a promising analytical framework to both study theoretical properties related to the information dissemination process and to devise efficient and reliable simulation schemes for very large systems

Information dissemination in mobile networks

This thesis proposes some solutions to relieve, using Wi-Fi wireless networks, the data consumption of cellular networks using cooperation between nodes, studies how to make a good deployment of access points to optimize the dissemination of contents, analyzes some mechanisms to reduce the nodes' power consumption during data dissemination in opportunistic networks, as well as explores some of the risks that arise in these networks. Among the applications that are being discussed for data off-loading from cellular networks, we can find Information Dissemination in Mobile Networks. In particular, for this thesis, the Mobile Networks will consist of Vehicular Ad-hoc Networks and Pedestrian Ad-Hoc Networks. In both scenarios we will find applications with the purpose of vehicle-to-vehicle or pedestrian-to-pedestrian Information dissemination, as well as vehicle-to-infrastructure or pedestrian-to-infrastructure Information dissemination. We will see how both scenarios (vehicular and pedestrian) share many characteristics, while on the other hand some differences make them unique, and therefore requiring of specific solutions. For example, large car batteries relegate power saving techniques to a second place, while power-saving techniques and its effects to network performance is a really relevant issue in Pedestrian networks. While Cellular Networks offer geographically full-coverage, in opportunistic Wi-Fi wireless solutions the short-range non-fullcoverage paradigm as well as the high mobility of the nodes requires different network abstractions like opportunistic networking, Disruptive/Delay Tolerant Networks (DTN) and Network Coding to analyze them. And as a particular application of Dissemination in Mobile Networks, we will study the malware spread in Mobile Networks. Even though it relies on similar spreading mechanisms, we will see how it entails a different perspective on Dissemination

Modelling spatio-temporal tree disease epidemics in Great Britain

Presently, tree populations worldwide face unprecedented threats from invasive pests and pathogens endangering biodiversity, timber production and human wellbeing. From first principles, this thesis incrementally extends a simple percolation model of forest-based epidemics into a more involved stochastic dispersal framework combined with tree canopy data. The approach developed here couples two spatially-explicit epidemic models at different scales. First, a non-local stochastic model of pathogen dispersal between trees is constructed. Second, the small-scale epidemic model is projected onto a large-scale distribution of host abundance, resulting in an $R_0$-map across Great Britain. Subsequently, a clustering algorithm is employed to identify high-risk regions in the $R_0$-map. Initial results indicate a global epidemic phase transition across the distribution, conditional on an infectivity parameter. The approach to `spatially scale-up' an epidemic model over the entire landscape is computationally efficient, flexible and adaptable to many pests and pathogens. In addition, numerous studies have sought to understand and optimise epidemic control in botanical populations. The mainstream control paradigm generally seeks to optimise an `eradication radius' about infected symptomatic trees over a relatively small spatial scale. However, large-scale epidemic control based solely on the spatial distribution of hosts has yet to be explored in depth. As such, this thesis will also examine how host heterogeneity, combined with targeted epidemic control, can give rise to natural `pinch-points' that may slow the epidemic spread between regions. Ultimately, this investigation intends to help policymakers reach informed decisions about where to focus control in the landscape of Great Britain