Publications at the Research Group "Communication Networks"

Results: 229
Created on: Sun, 24 Sep 2023 12:52:32 +0200 in 0.0653 sec

Izadi, Adel; Gholamhosseinian, Ashkan; Seitz, Jochen
VANET-based traffic light management for an emergency vehicle. - In: Ubiquitous networking, (2023), S. 129-137

Wireless communications have affected our lifestyle in the last decades. It is helpful to improve quality of life for communities. Communications among vehicles usually take place in vehicular ad-hoc networks (VANETs). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are aspects of communications in the transportation which are growing rapidly. They can play a pivotal role in the transportation field. Management of traffic lights (TLs) is crucial to control traffic flow especially at an intersection. The goal of this paper is to manage the TLs at an intersection when an emergency vehicle (EV) is approaching. First, we simulate an intersection which includes TLs via simulation of urban mobility (SUMO). Later, we simulate VANETs communication to manage the TLs at the intersection when the EV is coming with the help of objective modular network testbed in C++ (OMNeT++) and vehicles in network simulation (Veins). Finally, the impact of V2I communication on delivery efficiency of the emergency service is investigated. Simulation results show an improvement in delivery efficiency of the emergency service.

Samadi, Raheleh; Seitz, Jochen
Machine learning routing protocol in mobile IoT based on software-defined networking. - In: 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks, (2022), S. 108-111

The Internet's pervasive influence in all aspects of life has caused the number of heterogeneous devices connected to this network to grow exponentially. As a result, recognizing these devices and their management has led to the emergence of a new paradigm called the “Internet of Things” (IoT). Sensor networks are the essential pillar of the Internet of Things. Due to their low cost and ease of deployment, they can be implemented in a structured or unstructured way in a dynamic physical environment to manage and monitor the dynamic conditions of the desired area in various applications. Nevertheless, what is noteworthy in this regard is the limited resources of sensor networks, which cannot meet the diverse needs of the Internet of Things, so appropriate solutions must be adopted to some challenges, such as scalability and routing in dynamic topologies. Against this challenge, the SDN paradigm has attracted massive attention because it is possible to add new capabilities to networks with limited resources to reduce the overhead caused by processing and computing in sensor nodes and delegate these energy-consuming tasks to the controller. On the other hand, machine learning techniques have also shown their ability to optimize routing and increase the quality of service, reliability, and security by using statistics and information obtained from these networks. However, less research has addressed sensor nodes' mobility and challenges in resource-constrained IoT networks.
Gholamhosseinian, Ashkan; Seitz, Jochen
Versatile safe autonomous intersection management protocol for heterogeneous connected vehicles. - In: IEEE Xplore digital library, ISSN 2473-2001, (2022), insges. 7 S.

In this paper, we employ a novel safe centralized intersection management (IM) scheme for heterogeneous classes of connected vehicles such as passenger vehicles (PVs), buses, vans, trucks, emergency vehicles (EVs), trams and also vulnerable road users (VRUs) including motorcycles, bicycles, and moped. A rule-based algorithm undertakes safe coordination using different criteria such as intersection traffic rules, vehicle types and road priorities. Besides, in the proposed system, dynamics and behavior of the road users, which might be totally distinct, are taken into account. These underlying characteristic behaviors comprise of reaction distance $(D_r)$, stopping distance $(D_s)$, braking distance $(D_b)$, braking lag distance $(D_bl)$ as well as deceleration (d) and velocity (v) of the vehicles in an urban environment. In addition, special lanes for cyclists and tram tracks are considered in the layout. Furthermore, in order to more properly image the realistic traffic situations, we have integrated the impact of various road conditions in our system in terms of dry, wet, snowy, and icy. Performance of the system is analyzed in several sparse and dense traffic situations with respect to different criteria such as speed and travel time. Additionally, efficiency is also compared to traditional intersection management systems like traffic lights (TLs).
Alshra'a, Abdullah Soliman; Seitz, Jochen
One-dimensional convolutional neural network for detection and mitigation of DDoS attacks in SDN. - In: Machine learning for networking, (2022), S. 11-28

In Software-Defined Networking (SDN), the controller plane is an essential component in managing network traffic because of its global knowledge of the network and its management applications. However, an attacker might attempt to direct malicious traffic towards the controller, paralyzing the entire network. In this work, a One-Dimensional Convolutional Neural Network (1D-CNN) is used to protect the controller evaluating entropy information. Therefore, the CICDDoS2019 dataset is used to investigate the proposed approach to train and evaluate the performance of the model and then examine the effectiveness of the proposal in the SDN environment. The experimental results manifest that the proposed approach achieves very high enhancements in terms of accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) for the detection of Distributed Denial of Service (DDoS) attacks compared to one of the benchmarking state of the art approaches.

Gholamhosseinian, Ashkan; Seitz, Jochen
Empirical estimation of ETSI ITS-G5 performance over an IPv6-based platform. - In: 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC), (2022), S. 185-193

Intelligent transport systems (ITS) promise to leverage wireless communications among vehicles. Performance of wireless communication is of crucial importance and can severely impact safety, efficiency and infotainment of transport applications. In this paper, in order to promote the usage of IPv6 in vehicular networks, we deploy a wireless communication framework in a Linux environment based on the Internet protocol (IP). Further, we evaluate the behaviour of the ITS-G5 in various stationary and mobile scenarios to investigate the impact of environmental and radio factors on the performance of ITS-G5. Several measurements such as inter-reception time (IRT), packet loss, latency, end-to-end (E2E) latency, reception position error (RPE), and throughput are carried out to discover the limitations and strength of the technology in a real world test-bed.
Djuitcheu, Hubert; Debes, Maik; Aumüller, Matthias; Seitz, Jochen
Recent review of distributed denial of service attacks in the Internet of Things. - In: 5th Conference on Cloud and Internet of Things, (2022), S. 32-39

The use of the Internet of Things (IoT) in almost all domains nowadays makes it the network of the future. Due to the high attention since its creation, this network is the target of numerous attacks of different purpose and nature, of which one of the most perpetrated and virulent is the distributed denial of services (DDoS) attack. This article reviews the different security requirements and some of the attacks within IoT networks. It then focuses on DDoS attacks on the IoT and summarizes some methods of countermeasures for this attack, from the oldest to the most recent ones. Based on this study, it seems that the benefits of machine learning (ML) and deep learning (DL) combined with other technologies such as software defined networking (SDN) are very promising approaches against DDoS attacks.
Lak, Hadi Jalali; Gholamhosseinian, Ashkan; Seitz, Jochen
Distributed vehicular communication protocols for autonomous intersection management. - In: The 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40), (2022), S. 150-157

Intersections are considered to be a vital part of urban transportation and drivers are prone to make more mistakes, when driving through the intersections. A high percentage of the total fatal car accidents leading to injuries are reported within intersections annually. On the other side, there usually is traffic congestion at intersections during busy times of day. Stopping the vehicles in one direction to let the vehicle pass in the other directions leads to this phenomenon and it has a huge effect on traffic delay, which causes great squander in natural and human resources as well as leading to weather pollution in metropolises. The goal of this paper is to design and simulate different spatio-temporal-based algorithms for autonomous connected vehicles to be able to cross the intersection safely and efficiently. Vehicles employ vehicle-to-vehicle (V2V) communication via dedicated short range communications (DSRC) [4, 1] to exchange their kinematic information with each other. The proposed algorithms are compared to each other as well as with traditional methods like traffic lights in terms of various performance metrics such as traffic congestion, speed and especially delay to find the optimal control approach for autonomous intersection management.
Alshra'a, Abdullah Soliman;
Intrusion Detection System against Denial of Service attack in Software-Defined Networking. - Ilmenau : Universitätsbibliothek, 2022. - 1 Online-Ressource (vii, 153 Seiten)
Technische Universität Ilmenau, Dissertation 2022

Das exponentielle Wachstum der Online-Dienste und des über die Kommunikationsnetze übertragenen Datenvolumens macht es erforderlich, die Struktur traditioneller Netzwerke durch ein neues Paradigma zu ersetzen, das sich den aktuellen Anforderungen anpasst. Software-Defined Networking (SDN) ist hierfür eine fortschrittliche Netzwerkarchitektur, die darauf abzielt, das traditionelle Netzwerk in ein flexibleres Netzwerk umzuwandeln, das sich an die wachsenden Anforderungen anpasst. Im Gegensatz zum traditionellen Netzwerk ermöglicht SDN die Entkopplung von Steuer- und Datenebene, um Netzwerkressourcen effizient zu überwachen, zu konfigurieren und zu optimieren. Es verfügt über einen zentralisierten Controller mit einer globalen Netzwerksicht, der seine Ressourcen über programmierbare Schnittstellen verwaltet. Die zentrale Steuerung bringt jedoch neue Sicherheitsschwachstellen mit sich und fungiert als Single Point of Failure, den ein böswilliger Benutzer ausnutzen kann, um die normale Netzwerkfunktionalität zu stören. So startet der Angreifer einen massiven Datenverkehr, der als Distributed-Denial-of-Service Angriff (DDoSAngriff) von der SDN-Infrastrukturebene in Richtung des Controllers bekannt ist. Dieser DDoS-Angriff führt zu einer Sättigung der Steuerkanal-Bandbreite und belegt die Ressourcen des Controllers. Darüber hinaus erbt die SDN-Architektur einige Angriffsarten aus den traditionellen Netzwerken. Der Angreifer fälscht beispielweise die Pakete, um gutartig zu erscheinen, und zielt dann auf die traditionellen DDoS-Ziele wie Hosts, Server, Anwendungen und Router ab. In dieser Arbeit wird das Verhalten von böswilligen Benutzern untersucht. Anschließend wird ein Intrusion Detection System (IDS) zum Schutz der SDN-Umgebung vor DDoS-Angriffen vorgestellt. Das IDS berücksichtigt dabei drei Ansätze, um ausreichendes Feedback über den laufenden Verkehr durch die SDN-Architektur zu erhalten: die Informationen von einem externen Gerät, den OpenFlow-Kanal und die Flow-Tabelle. Daher besteht das vorgeschlagene IDS aus drei Komponenten. Das Inspector Device verhindert, dass böswillige Benutzer einen Sättigungsangriff auf den SDN-Controller starten. Die Komponente Convolutional Neural Network (CNN) verwendet eindimensionale neuronale Faltungsnetzwerke (1D-CNN), um den Verkehr des Controllers über den OpenFlow-Kanal zu analysieren. Die Komponente Deep Learning Algorithm(DLA) verwendet Recurrent Neural Networks (RNN), um die vererbten DDoS-Angriffe zu erkennen. Sie unterstützt auch die Unterscheidung zwischen bösartigen und gutartigen Benutzern als neue Gegenmaßnahme. Am Ende dieser Arbeit werden alle vorgeschlagenen Komponenten mit dem Netzwerkemulator Mininet und der Programmiersprache Python modelliert, um ihre Machbarkeit zu testen. Die Simulationsergebnisse zeigen hierbei, dass das vorgeschlagene IDS im Vergleich zu mehreren Benchmarking- und State-of-the-Art-Vorschlägen überdurchschnittliche Leistungen erbringt.
Samadi, Raheleh; Seitz, Jochen
EEC-GA: energy-efficient clustering approach using genetic algorithm for heterogeneous wireless sensor networks. - In: The 36th International Conference on Information Networking (ICOIN 2022), (2022), S. 280-286

In a wireless sensor network (WSN), sensor nodes are distributed in a predefined environment, collecting environmental data and transferring them to the base station or sink for processing and analysis. In the meantime, much research has been done on heterogeneous wireless sensor networks. Due to the fact that in a heterogeneous wireless sensor network (HWSN) factors such as primary energy, data processing ability, etc. greatly affect the life of the network, the selection of cluster heads (CHs) for coordinating a cluster of sensor nodes has created a wide range for further development of network efficiency capabilities. In this work, an event-driven energy-efficient protocol based on a genetic algorithm is proposed that uses various parameters such as the remaining energy of the node, the minimum distance of a node to the base station, and also the degree of the neighborhood of a node as fitness function values, and evaluates solutions in a heterogeneous wireless sensor network. For evaluating the proposal, network stability, energy consumption, death of the first node, and number of living nodes per round are considered as evaluation criteria.
Gholamhosseinian, Ashkan; Seitz, Jochen
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles. - In: IEEE access, ISSN 2169-3536, Bd. 10 (2022), S. 7937-7972