4 research outputs found

    Resource management for next generation multi-service mobile network

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    Human dynamic networks in opportunistic routing and epidemiology

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    Measuring human behavioral patterns has broad application across different sciences. An individual’s social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure. This thesis presents a data collection system using tiny sensors which is capable of recording humans’ proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants’ health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information. In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the model’s contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection

    Enabling Tetherless Care with Context-Awareness and Opportunistic Communication

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    Tetherless care is a novel healthcare delivery paradigm that enables an interaction between caregivers and patients beyond the confines of traditional points of care. This thesis presents a synthesis of recent advances in wearable, ubiquitous sensing; mobile computing; wireless networks; and health information technology into a cohesive framework that enables and supports the tetherless care concept. Tetherless care is formally defined and modeled in a higher order logical framework. The model distills three relations between several classes in the model's domain of discourse. A prototype implementation is developed and evaluated to capture and represent the logical classes of tetherless care and provide the development infrastructure upon which the relational logic outlined by the model can be implemented. An algorithm is presented and evaluated to support the delivery of traffic between mobile devices and servers despite intermittent connectivity given the changing urgency of the patient's situation. And an example tetherless care application is presented, developed for the framework, and compared with its deployment on a similar platform. Results show that contemporary mobile devices supply sufficient power to support 24 hours of operation and that, at least, some patient environments provide sufficient opportunities for connectivity to reliably meet the demands of some tetherless care applications, ultimately leading to a conclusion of proof-of-concept for tetherless care

    Modeling and Measuring Performance of Data Dissemination in Opportunistic Networks

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    In this thesis we focus on understanding, measuring and describing the performance of Opportunistic Networks (ONs) and their applications. An “opportunistic network” is a term introduced to describe a sparse, wireless, ad hoc network with highly mobile nodes. The opportunistic networking paradigm deviates from the traditional end-to-end connectivity concept: Forwarding is based on intermittent connectivity between mobile nodes (typically, users with wireless devices); complete routes between sources and destinations rarely exist. Due to this unique property of spontaneous link establishment, the challenges that exist in ONs are specific. The unstructured nature of these networks makes it difficult to give any performance guarantees on data dissemination. For this reason, in Part I of this thesis we explore the dynamics that affect the performance of opportunistic networks. We choose a number of meaningful scenarios where our models and algorithms can be validated using large and credible data sets. We show that a drift and jump model that takes a spatial approach succeeds in capturing the impact of infrastructure and mobile-to-mobile exchanges on an opportunistic content update system. We describe the effects of these dynamics by using the age distribution of a dynamic piece of data (i.e., information updates) as the performance measure. The model also succeeds in capturing a strong bias in user mobility and reveals the existence of regions, whose statistics play a critical role in the performance perceived in the network. We exploit these findings to design an application for greedy infrastructure placement, which relies on the model approximation for a large number of nodes. Another great challenge of opportunistic networking lies in the fact that the bandwidth available on wireless links, coupled with ad hoc networking, failed to rival the capacity of backbones and to establish opportunistic networks as an alternative to infrastructure-based networks. For this reason, we never study ONs in an isolated context. Instead, we consider the applications that leverage interconnection between opportunistic networks and legacy networks and we study the benefits this synergy brings to both. Following this approach, we use a large operator-provided data set to show that opportunistic networks (based on Wi-Fi) are capable of offloading a significant amount of traffic from 3G networks. At the same time, the offloading algorithms we propose reduce the amount of energy consumed by mobiles, while requiring Wi-Fi coverage that is several times smaller than in the case of real-time offloading. Again we confirm and reuse the fact that user mobility is biased towards certain regions of the network. In Part II of this thesis, we treat another issue that is essential for the acceptance and evolution of opportunistic networks and their applications. Namely, we address the absence of experimental results that would support the findings of simulation based studies. Although the techniques such as contact-based simulations should intuitively be able to capture the performance of opportunistic applications, this intuition has little evidence in practice. For this reason, we design and deploy an experiment with real users who use an opportunistic Twitter application, in a way that allows them to maintain communication with legacy networks (i.e., cellular networks, the Internet). The experiment gives us a unique insight into certain performance aspects that are typically hidden or misinterpreted when the usual evaluation techniques (such as simulation) are used. We show that, due to the commonly ignored factors (such as the limited transmission bandwidth), contact-based simulations significantly overestimate delivery ratio and obtain delays that are several times lower than those experimentally acquired. In addition to this, our results unanimously show that the common practice of assuming infinite cache sizes in simulation studies, leads to a misinterpretation of the effects of a backbone on an opportunistic network. Such simulations typically overestimate the performance of the opportunistic component, while underestimating the utility of the backbone. Given the discovered deficiencies of the contact-based simulations, we consider an alternative statistical treatment of contact traces that uses the weighted contact graph. We show that this approach offers a better interpretation of the impact of a backbone on an opportunistic network and results in a closer match when it comes to modeling certain aspects of performance (namely, delivery ratio). Finally, the security requirements for the opportunistic applications that involve an interconnection with legacy networks are also highly specific. They cannot be fully addressed by the solutions proposed in the context of autonomous opportunistic (or ad hoc) networks, nor by the security frameworks used for securing the applications with continuous connectivity. Thus, in Part III of this thesis, we put together a security framework that fits the networks and applications that we target (i.e., the opportunistic networks and applications with occasional Internet connectivity). We then focus on the impact of security print on network performance and design a scheme for the protection of optimal relaying capacity in an opportunistic multihop network. We fine-tune the parameters of our scheme by using a game-theoretic approach and we demonstrate the substantial performance gains provided by the scheme
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