Accelerating Veins Simulations by Utilizing Task Parallelism on a HPC Cluster Without Introducing Major Inaccuracies - Fritscher, Markus; Dengler, Gabriel; Bleibaum, Cord; Niebisch, Michael; German, Reinhard (13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), 2022)
The evaluation of large-scale vehicular networks has proven difficult. The researcher has to either do a lot of experiments, reduce simulation accuracy or endure simulation runtimes prohibiting a reasonable investigation. We present a framework that allows preserving simulation accuracy while maintaining reasonable simulation times by cutting the to-be-investigated area into individual tiles. Instead of running on a single CPU core we are able to run the simulation on hundreds of HPC servers in parallel. This yields a speedup of several orders of magnitude, allowing the simulation of entire cities. We validate our approach by investigating different access-point distributions for the city of Ingolstadt.<br
Keywords: SUMO; OMNeT++; Veins; HPC; simulation

CoDiPy: Performance Evaluation of Vehicular Cooperative Downloading in Python - Niebisch, Michael; Pfaller, Daniel; Djanatliev, Anatoli (18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2022), 2022)
With the increase of vehicular software, the need for efficient and cost-effective communication arises. As cellular communication alone can introduce large monetary costs, alternative communication schemes, like cooperative downloading, have been proposed. Thereby, vehicles can use direct communication technologies to collect a full set of messages. Existing network simulators can be used to evaluate the performance of such systems, however, their accurate transmission model impedes the performance. To circumvent the performance gap, we introduce Cooperative Downloading in Python (CoDiPy) to increase the simulation performance and enable large-scale and long simulation runs. We evaluate the accuracy of our results against a state-of-the-art network simulator with the same transmission parameters. CoDiPy's improves the execution time in our experiments by a factor of up to 36 when compared to a network simulator, without inducing inaccuracies.<br
Keywords: Cooperative Downloading; Veins; OMNet++; vehicular communication

Cooperative Downloading in Heterogeneous Vehicular Networks: A Cost Analysis - Niebisch, Michael; Pfaller, Daniel; Djanatliev, Anatoli (2022 International Conference on Electronics, Information, and Communication (ICEIC) (ICEIC 2022), 2022)
Vehicular communication technologies play a major role in next-generation cars. Over-the-air (OTA) updates supply the vehicles with software updates via cellular communication networks. With the increasing amount of software in the vehicle, the cost of only relying on cellular communication can be high. However, other technologies like direct communication could be used to lower the cost of data transfer. Moreover, as car manufacturers have moved to cooperate, e.g., on their electric architecture and map data, identical data packets are sent to many vehicles. We, therefore, propose a cooperative downloading strategy, utilizing a heterogeneous Vehicle-to-Everything (V2X) network and analyze subsequent costs. We aim to reduce the cost of data communication by incorporating multiple technologies with our data dissemination strategy. Results from our simulation framework show cost reductions of up to 80% with the introduction of data exchange between vehicles and moderate deadlines.
Keywords: heterogeneous networks; opportunistic communication; cooperative downloading

Drivers and Challenges of Open Data from an Energy Industry Perspective - Speck, Christina; Herrmann, Selina; Pan, Zhiyu; Sedighi, Foroogh; Seiwerth, Corinna; Niebisch, Michael; Weinhardt, Christof (1. NFDI4Energy Conference, 2024)
Open data plays a crucial role in the information society, offering numerous advantages. Nevertheless, it poses significant challenges, particularly within the energy sector. This paper explores the drivers and challenges of open data through a literature review. Additionally, we outline our methodology using design science to further elucidate these insights in collaboration with stakeholders from the energy industry, actively working towards practical solutions.
Keywords: open data; energy industry; design science

Enhancing Vehicular Cooperative Downloading with Continuous Seeding through Deep Reinforcement Learning - Niebisch, Michael; Pfaller, Daniel; Djanatliev, Anatoli (2023 IEEE Latin-American Conference on Communications (LATINCOM), 2023)
Vehicular communication needs are steadily increasing. While some data is unique for every vehicle, other data, like updates, have to be distributed to many cars. However, the sole reliance on cellular networks unnecessarily increases data costs and data usage. A vehicular cooperative downloading scheme utilizes additional communication technologies, like direct communication between vehicles, to exchange data. For an efficient use of this communication channel, enough data has to be present in the vehicles, which can be exchanged. A seeding strategy introduces parts of the update into the vehicles to start the cooperative downloading procedure. However, if too few data chunks are present, the direct communication link is not fully utilized, while over-provisioning the vehicles with data unnecessarily increases cellular usage. In this paper, we first introduce an initial seeding strategy and find its optimal results for a baseline comparison. We then use deep reinforcement learning to develop a strategy which continuously determines how much data needs to be added to the vehicle fleet at any time, optimizing the overall system's performance. Our results consider various update sizes and mobility scenarios and show that proximal policy optimization is a suitable approach to lower the usage of cellular communication in a cooperative downloading scheme. We can reduce the use of cellular networks by more than 98%, while continuously seeding improves the best initial seeding approach by more than 60%.
Keywords: reinforcement learning; cooperative downloading; vehicular communication; data dissemination

GRETEL: Graph-based Street Coverage Calculation for Vehicle-to-Infrastructure Communication - Niebisch, Michael; German, Reinhard; Djanatliev, Anatoli (16th Conference on Wireless On-demand Network Systems and Services (WONS 2021), 2021)
Connected vehicles will exchange large amounts of data with remote systems. This can include recent traffic information, search results, or software updates to the car. Data communications can, therefore, be expensive as currently all transmissions to and from the vehicles are done via mobile service providers. Wireless Local Area Network (WLAN) offloading to home deployed Access Points (APs) for internet connectivity can be a more affordable option, as the use of the frequency spectrum is free of charge. While the technology is already widely deployed, the outdoor availability on the street is critical for the use in vehicles. To analyze the street coverage we have developed GRETEL, a GRaph-based streET covErage caLculation. GRETEL enables the analysis of Vehicle-to-Infrastructure (V2I) communications by combining location data with signal measurements mapped to the relevant road topology. To test GRETEL's performance, we conducted test drives to measure WLAN beacon frames and identify receivable WLAN APs. We determine that more than 9 % of all APs in our data set are commercially available connection options. With GRETEL, we could determine that the subset of commercially available APs covers about 60 % of the street network. Therefore, we introduced a novel method for street coverage calculations called GRETEL and showed encouraging results for the use of commercially available WLAN APs for V2I communication
Keywords: V2I; coverage calculation; road coverage; graph-based calculation; field test

Impact of the Communication Direction on the Reliability of Vehicle-to-Everything (V2X) Communications - Niebisch, Michael; Deinlein, Thomas; Pfaller, Daniel; German, Reinhard; Djanatliev, Anatoli (Proceedings of the 12th IEEE Vehicular Networking Conference (VNC 2020), 2020)
Communications between vehicles and their surroundings can improve the safety of road users. Other vehicles can be identified via radio broadcast messages and the safety in non-line-of-sight situations can be improved. One emerging technology is Cellular Vehicle-to-Everything (C-V2X) communications, which enables various direct communications applications, mainly safety use cases. Broadcast messages are received by all road users in the vicinity, while signal propagation determines the maximum distance over which messages can be decoded. Field tests often use test antennas, set up on the vehicles' roof to test the communications link. While this is suitable for testing general use cases, in a real-life deployment of the technology, the antenna placement will have to comply with design aspects. In this paper we have conducted different field tests with an antenna prototype integrated into the existing shark fin housing. We measured the achievable packet error rates (PER) for different distances and communication directions. The influence of the car on the transmission of radio waves asymmetrically affects the propagation. The maximum distance for reliable communication in C-V2X is influenced by characteristics and placement of the antenna and the shape of the vehicle. Additionally, we could observe a significant impact by panorama glass roofs. Antenna placement will, therefore, be an important factor for the capability of V2X communications.<br
Keywords: V2X; field tests; C-V2X; antenna placement; PC5

On the Benefits of Opportunistic WiFi in Cooperative Downloading - Niebisch, Michael; Pfaller, Daniel; German, Reinhard; Djanatliev, Anatoli (2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023)
With the increased use of software in modern vehicles, the need for updates and data communication rises. While many vehicles are equipped with cellular communication technology, monetary costs are adding up for vehicle manufacturers. Cooperative downloading is a communication scheme, in which many vehicles cooperatively download an update and share the data between them. Through the use of direct communication, only small amounts of data have to be transferred from the internet to the vehicle. Besides using cellular communication for the initial transfer of data, the opportunistic use of WiFi access points has been proposed. To accurately evaluate the usefulness of access points, it is imperative to model their availability, which requires measurement data. We have used an extensive data set and introduced a novel approach to model the reception of access points on a street network. We have shown that even reduced connectivity with the WiFi access points benefits the cooperative downloading scheme, while the deployment of direct communication in the vehicles has larger benefits for the overall update progress. However, if WiFi is used as an additional technology in a cooperative downloading scheme, even its opportunistic availability greatly improves the overall system.<br
Keywords: cooperative downloading; WiFi; opportunistic communication; access point modeling

Performance Improvements in Cooperative Downloading: Encoding and Strategies for Heterogeneous Vehicular Networks - Niebisch, Michael; Pfaller, Daniel; German, Reinhard; Djanatliev, Anatoli (IEEE 47th Conference on Local Computer Networks (LCN), 2022)
With the vast increase in software and digital services in the vehicular domain, communication networks play an increasingly important role. While some applications rely on fast data transfers, other tasks can be offloaded and delayed. For those, cooperative downloading schemes have been proposed, which utilize opportunistic communication, heterogeneous networks, and extended deadlines in a best effort approach. The efficiency of such communication depends on the chosen protocol and strategies, which impact the performance. We, therefore, introduce a novel hybrid encoding scheme for the encoding of relevant data. Additionally, we study the impact of reacting to data requests and initial distribution of data. We introduce a protocol logic for cooperative downloading and evaluate the overall downloading efficiency in simulation. A comparison between strategies reveals improvements of up to 31.8% in downloading progress and a time reduction averaging 46.4% for the best set of parameters, while also reducing the channel load significantly.<br
Keywords: cooperative downloading; opportunistic communication; heterogeneous vehicular networks

Research Software Engineering in the Energy Domain as Part of NFDI4Energy - Seiwerth, Corinna; Niebisch, Michael; Ferenz, Stephan; German, Reinhard (Conference for Research Software Engineering in Germany, 2024)
Research software presents an important research artifact in energy research. Therefore, the National Research Data Infrastructure for the Interdisciplinary Energy System Research (NFDI4Energy) addresses the handling of research software across the entire research and transfer cycle within energy system research projects. For this, the FAIR data principles are applied not only to data but also to softwar

Self-Navigating Automotive Production: Using 5G for Automated Driving in Car Assembly - Ambrosy, Niklas; Rainer, Julia; Niebisch, Michael; German, Reinhard; Underberg, Lisa (2022 IEEE 18th International Conference on Factory Communication Systems (WFCS), 2022)
5G is on the rise in both automated driving and industrial automation. So far, these application areas have been considered separately, although the automotive industry can apply them jointly. We examine the future car factory use case Self-Navigating Automotive Production (SNAP), which derives from Automated Valet Parking (AVP). While SNAP benefits from increased flexibility and efficiency, current issues include the lack of AVP standardization and higher system complexity in the factory due to additional cameras, software, as well as worker and machine collaboration. Our concept represents a novel solution for car production and combines both ecosystems.
Keywords: 5G; V2X; Automated Valet Parking; Automated Factory Parking; automotive production

Towards a Data-Centric Distributed Simulation Framework for the Energy Domain - Seiwerth, Corinna; Niebisch, Michael; German, Reinhard (1. NFDI4Energy Conference, 2024)
Existing simulation tools from the energy domain are often limited to specific problems in the energy sector. Co-simulation frameworks enable the combination of these models and, thus, a more realistic simulation of the energy domain. This paper analyzes some co-simulation frameworks from the energy domain by evaluating them according to specific criteria. Since none of the presented frameworks fulfills all the criteria, we then present a data-centric distributed simulation framework and how we extend it for the energy domain.<br
Keywords: Co-Simulation; Data-Centric Distributed Simulation; Energy System Mod- eling

Transparency and Involvement of the Energy-Related Industry in a Data Sharing Platform - Pan, Zhiyu; Gürses-Tran, Gonca; Speck, Christina; Jaquart, Patrick; Niebisch, Michael; Monti, Antonello (Vol. 1 (2023): 1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities, 2023)
The integration of renewable energy sources, the decentralization of the energy system, and the increasing digitization of energy-related processes require the integration of a wide range of energy-related data. In this context, a data sharing platform can serve as a hub for exchanging energy-related data and developing innovative solutions to improve the efficiency and sustainability of the energy system. However, especially because of the involvement of the energy-related industry in such a platform poses several challenges related to data protection, intellectual property, and business interests. This paper presents a framework for ensuring transparency and involvement of the energy-related industry in a data sharing platform, based on the FAIR data principles and a co-creation approach involving industry partner
Keywords: Data sharing platform; FAIR principles; energy industry