Publications at the Research Group "Communication Networks"

Results: 235
Created on: Sun, 19 May 2024 12:57:39 +0200 in 0.0758 sec


Gholamhosseinian, Ashkan; Seitz, Jochen
CAI2M2: a centralized autonomous inclusive intersection management mechanism for heterogeneous connected vehicles. - In: IEEE open journal of vehicular technology, ISSN 2644-1330, Bd. 5 (2024), S. 230-243

https://doi.org/10.1109/OJVT.2024.3354393
Heubach, Michael; Seitz, Jochen; Rink, Wolfram
17. Ilmenauer TK-Manager Workshop : Technische Universität Ilmenau, 29. September 2023 : Tagungsband. - Ilmenau : ilmedia, 2023. - 1 Online-Ressource (17 Seiten)
https://doi.org/10.22032/dbt.57781
Gholamhosseinian, Ashkan; Seitz, Jochen
PCIMS: plenary centralized intersection management scheme for heterogeneous connected vehicles. - In: 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), (2023), insges. 6 S.

This paper proposes a novel plenary centralized intersection management scheme (PCIMS) for a non-signalized intersection. Our contributions are fourfold. First, we take into account vehicle heterogeneity by integrating various vehicle classes in the system. Second, we consider some key elements in the scheduling mechanism such as vehicle types, intersection rules and road priorities. Third, we discuss the significant role of different road conditions, specific vehicles behavior and safety parameters in the traffic safety. Forth, in addition to the traffic safety assurance, we demonstrate the superiority of the system performance over conventional traffic lights (TLs) in several scenarios with congested and sparse traffic in terms of average travel time (ATT), packet loss rate (PLR), channel busy rate (CBR), intersection busy time (IBT), and throughput.



https://doi.org/10.1109/COINS57856.2023.10189259
Gholamhosseinian, Ashkan; Seitz, Jochen
Plenary autonomous intersection management protocol for heterogeneous connected vehicles. - In: ICUFN 2023, (2023), S. 334-336

This paper proposes a centralized autonomous intersection management scheme for heterogeneous connected vehicles (HCVs). Contributions of this work are as follows. First, we sustainably classify heterogeneous vehicles with their distinctive safety-related characteristics. Second, we conduct a safe and efficient coordination algorithm with respect to some criteria such as vehicle types, road priorities and right of way rules. Third, we consider the impact of different road conditions, vehicle characteristics, load, and braking technology on the system performance. Forth, we demonstrate the efficiency of the system under various traffic densities with symmetric and asymmetric vehicle distribution. Besides, system performance is to be compared with traffic lights (TLs) scenarios in terms of throughput, average travel time (ATT), intersection busy time (IBT), channel busy rate (CBR), and packet loss rate (PLR) in various road conditions.



https://doi.org/10.1109/ICUFN57995.2023.10200347
Samadi, Raheleh; Nazari, Amin; Seitz, Jochen
Intelligent Energy-aware Routing Protocol in Mobile IoT Networks based on SDN. - In: IEEE transactions on green communications and networking, ISSN 2473-2400, Bd. 7 (2023), 4, S. 2093-2103

Intelligent devices and equipment have affected almost all aspects of our life and behavior. The type of connection and the manner of communication between this large volume of devices has caused the emergence of a vast field in the Internet called the Internet of Things, which significantly highlights the issue of energy management and increases the lifetime of networks. Complex communications, especially in mobile networks, have generated many challenges for network designers. To solve these challenges, the Software Defined Networking (SDN) paradigm has reduced the overhead in the devices caused by processing and computing by adding new capabilities to mobile IoT networks. This technique transfers energy-consuming tasks to the central controller, which manages continuous topological changes of the network in dynamic environments. This paper presents a new routing approach called Intelligent Energy-aware Routing Protocol in Mobile IoT Networks based on SDN (IERMIoT), which tries to manage the dynamic changes of topology due to the movement of mobile nodes to increase the network’s lifetime and prevent energy dissipation. For this purpose, it defines clusters of nodes and uses an intelligent evolutionary algorithm to determine the number of clusters required in the network and their balanced distribution in the dynamic environment. Also, this approach considers a mechanism to reduce the overhead of control packets and routing packets, which significantly affects the energy consumption of nodes. The simulation results indicate the proposed solution’s effectiveness compared to other simulated approaches with respect to packet delivery rate, average energy consumption, network lifetime, number of alive nodes, coverage, and routing overhead.



https://doi.org/10.1109/TGCN.2023.3296272
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.



https://doi.org/10.1109/NFV-SDN56302.2022.9974791
Gholamhosseinian, Ashkan; Seitz, Jochen
Versatile safe autonomous intersection management protocol for heterogeneous connected vehicles. - In: 2022 IEEE Vehicle Power and Propulsion Conference (VPPC), (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).



https://doi.org/10.1109/VPPC55846.2022.10003461
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.