331 research outputs found

    Enabling Distributed Simulation of OMNeT++ INET Models

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    Parallel and distributed simulation have been extensively researched for a long time. Nevertheless, many simulation models are still executed sequentially. We attribute this to the fact that many of those models are simply not capable of being executed in parallel since they violate particular constraints. In this paper, we analyze the INET model suite, which enables network simulation in OMNeT++, with regard to parallelizability. We uncovered several issues preventing parallel execution of INET models. We analyzed those issues and developed solutions allowing INET models to be run in parallel. A case study shows the feasibility of our approach. Though there are parts of the model suite that we didn't investigate yet and the performance can still be improved, the results show parallelization speedup for most configurations. The source code of our implementation is available through our web site at code.comsys.rwth-aachen.de.Comment: Published in: A. F\"orster, C. Sommer, T. Steinbach, M. W\"ahlisch (Eds.), Proc. of 1st OMNeT++ Community Summit, Hamburg, Germany, September 2, 2014, arXiv:1409.0093, 201

    Simulation of Mixed Critical In-vehicular Networks

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    Future automotive applications ranging from advanced driver assistance to autonomous driving will largely increase demands on in-vehicular networks. Data flows of high bandwidth or low latency requirements, but in particular many additional communication relations will introduce a new level of complexity to the in-car communication system. It is expected that future communication backbones which interconnect sensors and actuators with ECU in cars will be built on Ethernet technologies. However, signalling from different application domains demands for network services of tailored attributes, including real-time transmission protocols as defined in the TSN Ethernet extensions. These QoS constraints will increase network complexity even further. Event-based simulation is a key technology to master the challenges of an in-car network design. This chapter introduces the domain-specific aspects and simulation models for in-vehicular networks and presents an overview of the car-centric network design process. Starting from a domain specific description language, we cover the corresponding simulation models with their workflows and apply our approach to a related case study for an in-car network of a premium car

    The Quest for Scalability and Accuracy in the Simulation of the Internet of Things: an Approach based on Multi-Level Simulation

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    This paper presents a methodology for simulating the Internet of Things (IoT) using multi-level simulation models. With respect to conventional simulators, this approach allows us to tune the level of detail of different parts of the model without compromising the scalability of the simulation. As a use case, we have developed a two-level simulator to study the deployment of smart services over rural territories. The higher level is base on a coarse grained, agent-based adaptive parallel and distributed simulator. When needed, this simulator spawns OMNeT++ model instances to evaluate in more detail the issues concerned with wireless communications in restricted areas of the simulated world. The performance evaluation confirms the viability of multi-level simulations for IoT environments.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2017

    Component based approach using OMNeT++ for Train Communication Modeling

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    International audienceThis paper reports on our experience in using OMNeT++ to develop a network simulator focused on railway environments. Common design problems are analyzed, making emphasis on radio communication models. Scalability issues are raised when modeling the large topologies that are associated with railway communications. Our conclusions point out that model re-usability must be reinforced and that a component-based design must be adopted in order to build a tool for generating valuable performance results

    Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication

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    Future in-vehicular networks will be based on Ethernet. The IEEE Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time requirements in future car communication. Software-Defined Networking (SDN) extends the Ethernet control plane with a programming option that can add much value to the resilience, security, and adaptivity of the automotive environment. In this work, we derive a first concept for combining Software-Defined Networking with Time-Sensitive Networking along with an initial evaluation. Our measurements are performed via a simulation that investigates whether an SDN architecture is suitable for time-critical applications in the car. Our findings indicate that the control overhead of SDN can be added without a delay penalty for the TSN traffic when protocols are mapped properly.Comment: To be published at IEEE VTC2019-Sprin

    5G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniques

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    The development and implementation of Internet of Things (IoT) devices have been accelerated dramatically in recent years. As a result, a super-network is required to handle the massive volumes of data collected and transmitted to these devices. Fifth generation (5G) technology is a new, comprehensive wireless technology that has the potential to be the primary enabling technology for the IoT. The rapid spread of IoT devices can encounter many security limits and concerns. As a result, new and serious security and privacy risks have emerged. Attackers use IoT devices to launch massive attacks; one of the most famous is the Distributed Denial of Service (DDoS) attack. Deep Learning techniques have proven their effectiveness in detecting and mitigating DDoS attacks. In this paper, we applied two Deep Learning algorithms Convolutional Neural Network (CNN) and Feed Forward Neural Network (FNN) in dataset was specifically designed for IoT devices within 5G networks. We constructed the 5G network infrastructure using OMNeT++ with the INET and Simu5G frameworks. The dataset encompasses both normal network traffic and DDoS attacks. The Deep Learning algorithms, CNN and FNN, showed impressive accuracy levels, both reaching 99%. These results underscore the potential of Deep Learning to enhance the security of IoT devices within 5G networks

    Simu5G – An OMNeT++ library for end-to-end performance evaluation of 5G networks

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    In this paper we introduce Simu5G, a new OMNeT++-based model library to simulate 5G networks. Si-mu5G allows users to simulate the data plane of 5G New Radio deployments, in an end-to-end perspective and including all protocol layers, making it a valuable tool for researchers and practitioners interested in the performance evaluation of 5G networks and services. We discuss the modelling of the protocol layers, network entities and functions, and validate our abstraction of the physical layer using 3GPP-based sce-narios. Moreover, we show how Simu5G can be used to evaluate Multi-access Edge Computing (MEC) and Cellular Vehicle-to-everything (C-V2X) services offered through a 5G network
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