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PHOENIX: A Premise to Reinforce Heterogeneous and Evolving Internet Architectures with Exemplary Applications

dc.contributor.advisorFu, Xiaoming Prof. Dr.
dc.contributor.authorAdhatarao, Sripriya Srikant
dc.titlePHOENIX: A Premise to Reinforce Heterogeneous and Evolving Internet Architectures with Exemplary Applicationsde
dc.contributor.refereeFu, Xiaoming Prof. Dr.
dc.description.abstractengThe Internet was designed primarily for sharing expensive resources through establishing point-to-point connection using dedicated IP addresses. However, it has since evolved both in complexity and its number of users. Subsequently, various heterogeneous networking architectures and protocols have emerged to fulfill the growing needs of users. Information-Centric Networking (ICN) is a promising new networking paradigm which treats content as the first-class entity. In ICN, nodes exchange information based on the Names of the content instead of the IP addresses of the end points that request or provide the information. Internet of Things (IoT) has gained popularity in recent years and billions of devices are expected to connect in the near future. With advancements in technology and availability of Big data, Artificial Intelligence is producing promising new applications and services. Additionally, many new architectures and services like data centers, Cloud computing, Edge/Fog computing, etc., have also emerged to enhance the Internet by providing cost-effective, flexible and scalable computing platforms for the growing number of applications. However, nowadays, users are more interested in retrieving content(s) of their interest irrespective of its location. While, devices in the Sensor Networks need a larger address space and low communication overhead due to constrained resources. In addition, machine learning applications demand high computing power and often applications have to find a trade-off between resource consumption and desired accuracy. Moreover, these heterogeneous networks have different requirements and hence, they operate with dissimilar protocols. Additionally, there are many important open issues within these heterogeneous networks like naming and mobility in ICN, pub/sub and protocol translation in IoT, optimizing resource-accuracy trade off with machine learning, etc., that need to be resolved. This dissertation analyzes the heterogeneous and evolving Internet architectures research space and identifies six main open issues and reinforces the Internet with efficient and optimal solutions. In particular, the dissertation analyzes the two most important naming schemas in ICN: hierarchical and flat names through examining the various interlinked metrics and proposes the optimal choices for efficient naming schemas at the application and network layers in ICN. This is followed by a proposal for a gateway and additional functions to seamlessly integrate the heterogeneous Sensor Networks to the Internet to realize IoT. Additionally, we study the requirements of resource-constrained IoT devices and provide an efficient lightweight pub/sub system to optimize the resource utilization in IoT networks. We further provide a comprehensive and robust solution to support the network mobility in ICN through a distributed Mobility Agent service architecture followed by two distinct frameworks to address the open issues in deep learning 1) EVA: which provides a distributed architecture to optimize the resource-accuracy tradeoff associated with supervised deep learning and 2) ADA: which provides an architecture to detect security breaches in real-time using unsupervised deep learning. The performance of the proposed solutions and architectures in PHOENIX are evaluated via relevant applications with detailed observations. For each application, we meticulously device the suitable experiments to measure the overall performance of the proposed solution. Overall, the results obtained from the various experiments show that proposed solutions effectively resolve the open issues and reinforce the heterogeneous architectures in the Internet with efficient solutions. Furthermore, the results from the evaluations also show significant improvements in comparison with state-of-the-art approaches along with considerable enhancement in the performance of the network and the respective
dc.contributor.coRefereeHogrefe, Dieter Prof. Dr.
dc.contributor.thirdRefereeArumaithurai, Mayutan PD Dr.
dc.contributor.thirdRefereeBaum, Marcus Prof. Dr.
dc.contributor.thirdRefereeWaack, Stephan Prof. Dr.
dc.subject.engICN, IoT, CCN, NDN, Neural Networks, Video, Mobility, Pub/Sub, Edgede
dc.affiliation.instituteFakultät für Mathematik und Informatikde
dc.subject.gokfullInformatik (PPN619939052)de

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