Enhanced Forwarding Strategies in Information Centric Networking

Content Centric Networking (CCN), a Clean Slate architecture to Information Centric Networking (ICN) , uses new approaches to routing named content, achieving scalability, security and performance. This thesis proposes a design of an effective multi-path forwarding strategy and performs an evaluation of this strategy in a set of scenarios that consider large scale deployments. The evaluations show improved performance in terms of user application throughput, delays, adoptability and scalability against adverse conditions (such as differing background loads and mobility) compared to the originally proposed forwarding strategies. Secondly, this thesis proposes an analytical model based on Markov Modulated Rate Process (MMRP) to characterize multi-path data transfers in CCN. The results show a close resemblance in performance between the analytical model and the simulation model

Demo: Simulation-as-a-Service to Benchmark Opportunistic Networks

Repeatability, reproducibility, and replicability are essential aspects of experimental and simulation-driven research. Use of benchmarks in such evaluations further assists corroborative performance evaluations. In this work, we present a demonstrator of a simulation service, called ”OPS on the bench” which tackles these challenges in performance evaluations of opportunistic networks

A novel data dissemination model for organic data flows

The number of computing devices of the IoT are expected to grow exponentially. To address the communication needs of the IoT, research is being done to develop new networking architectures and to extend existing architectures. An area that lacks attention in these efforts is the emphasis on utilisation of omnipresent local data. There are a number of issues (e.g., underutilisation of local resources and dependence on cloud based data) that need to be addressed to exploit the benefits of utilising local data. We present a novel data dissemination model, called the Organic Data Dissemination (ODD) model to utilise the omni-present data around us, where devices deployed with the ODD model are able to operate even without the existence of networking infrastructure. The realisation of the ODD model requires innovations in many different area including the areas of opportunistic communications, naming of information, direct peer-to-peer communications and reinforcement learning. This paper focuses on highlighting the usage of the ODD model in real application scenarios and the details of the architectural components

Simulating Opportunistic Networks: Survey and Future Directions

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works[EN] Simulation is one of the most powerful tools we have for evaluating the performance of opportunistic networks (OppNets). In this paper, we focus on available tools and mod- els, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art OppNet simulations and sketch out possible further development and lines of research. This paper is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.This work was supported in part by the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R, in part by the Universidad Laica Eloy Alfaro de Manabi, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion, Ecuador. (Corresponding author: Jens Dede.)Dede, J.; Förster, A.; Hernández-Orallo, E.; Herrera-Tapia, J.; Kuladinithi, K.; Kuppusamy, V.; Manzoni, P.... (2018). Simulating Opportunistic Networks: Survey and Future Directions. IEEE Communications Surveys & Tutorials. 20(2):1547-1573. https://doi.org/10.1109/COMST.2017.2782182S1547157320

Informations-basierte Netze

Content Centric Networking (CCN), a "Clean Slate" architecture to Information Centric Networking (ICN) , uses new approaches to routing named content, achieving scalability, security and performance. This thesis proposes a design of an effective multi-path forwarding strategy and performs an evaluation of this strategy in a set of scenarios that consider large scale deployments. The evaluations show improved performance in terms of user application throughput, delays, adoptability and scalability against adverse conditions (such as differing background loads and mobility) compared to the originally proposed forwarding strategies. Secondly, this thesis proposes an analytical model based on Markov Modulated Rate Process (MMRP) to characterize multi-path data transfers in CCN. The results show a close resemblance in performance between the analytical model and the simulation model

On the Resilience of Opportunistic Networks against DoS Attacks

Opportunistic Networks (OppNets) enable contact-based networking and service provisioning when no infrastructure exists, e.g., in disaster areas. In such sensitive scenarios, maintaining their availability is important, but most existing work on OppNets mainly assume fully cooperative and thus not malicious nodes. In this paper, we study the impact of different flavors of low-intensity Denial of Service (DoS) attacks on OppNets, which are hard to detect and to counter. Our results indicate that low-rate DoS and black hole attacks as a special case of DoS, seem to have a huge impact on the packet delivery ratio and the delivery delay of an OppNet