331 research outputs found
Enabling Distributed Simulation of OMNeT++ INET Models
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
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
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
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
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
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
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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