TY - CHAP A1 - Nunes, Danilo Weber A1 - Volbert, Klaus T1 - A Wireless Low-power System for Digital Identification of Examinees (Including Covid-19 Checks) T2 - Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS), 07.02.2022 - 08.02.2022 N2 - Indoor localization has been, for the past decade, a subject under intense development. There is, however, no currently available solution that covers all possible scenarios. Received Signal Strength Indicator (RSSI) based methods, although the most widely researched, still suffer from problems due to environment noise. In this paper, we present a system using Bluetooth Low Energy (BLE) beacons attached to the desks to localize students in exam rooms and, at the same time, automatically register them for the given exam. By using Kalman Filters (KFs) and discretizing the location task, the presented solution is capable of achieving 100% accuracy within a distance of 45cm from the center of the desk. As the pandemic gets more controlled, with our lives slowly transitioning back to normal, there are still sanitary measures being applied. An example being the necessity to show a certification of vaccination or previous disease. Those certifications need to be manually checked for everyone entering the university’s building, which requires time and staff. With that in mind, the automatic check for Covid certificates feature is also built into our system. KW - Indoor Navigation KW - Indoor Localisation KW - Low-power Devices KW - Internet of Things KW - RSSI KW - BLE Beacons Y1 - 2022 SN - 978-989-758-551-7 U6 - https://doi.org/10.5220/0010912800003118 SP - 51 EP - 59 PB - SCITEPRESS - Science and Technology Publications ER - TY - CHAP A1 - Thelen, Simon A1 - Eder, Friedrich A1 - Melzer, Matthias A1 - Nunes, Danilo Weber A1 - Stadler, Michael A1 - Rechenauer, Christian A1 - Obergrießer, Mathias A1 - Jubeh, Ruben A1 - Volbert, Klaus A1 - Dünnweber, Jan T1 - A Slim Digital Twin For A Smart City And Its Residents T2 - SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam N2 - In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by “live” measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a “digital energy twin” platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data. KW - smart city KW - AI KW - digital twin KW - artificial intelligence KW - urban planning KW - BIM KW - portal system Y1 - 2023 SN - 979-8-4007-0891-6 U6 - https://doi.org/10.1145/3628797.3628936 SP - 8 EP - 15 PB - ACM ER - TY - CHAP A1 - Legler, Katharina A1 - Jajja, Muhammad Sheheryar A1 - Volbert, Klaus T1 - Analysis and Design of Smart Components in Digital Energy Twins T2 - Proceedings of the 10th International Conference on Internet of Things, Big Data and Security., Porto, Portugal April 6-8, 2025 N2 - The energy crisis, energy demand growth, and dependence on fossil fuels worldwide have made urgent action necessary for us to seek sustainability in energy production and use. Digital technologies, especially Digital Energy Twins, have immense potential to reduce energy consumption, thereby reducing environmental impacts, particularly in the building sector. This paper presents the development of a digital energy twin that supports sustainable energy consumption analysis and optimization. Our study begins with a comprehensive analysis of the energy consumption data, the weather data, and the building plans as a solid basis for the analysis. We identify key energy consumption trends and patterns across different timescales and device-specific details that could be optimized, such as base load consumption and device-specific inefficiencies. A key part of our work is forecasting energy consumption using time series models, such as the ARIMA model, which promises to be useful in identify ing patterns for improving energy efficiency. Overall, our study provides valuable insights into energy optimization and could form the base for further advances in digital energy twins at OTH Regensburg, helping to contribute to its sustainable development goals and smart campus initiatives. KW - Data Visualization KW - Digital Twins KW - Internet of Things KW - Machine Learning Models Y1 - 2025 SN - 978-989-758-750-4 U6 - https://doi.org/10.5220/0013289900003944 SP - 263 EP - 272 PB - SciTePress - Science and Technology Publications CY - Setúbal, Portugal ER - TY - INPR A1 - Köster, Dominik A1 - Porkert, Florian A1 - Volbert, Klaus T1 - Multi-Day Scheduling for Electric Vehicle Routing: A Novel Model and Comparison Of Metaheuristics N2 - The increasing use of electric vehicles (EVs) requires efficient route planning solutions that take into account the limited range of EVs and the associated charging times, as well as the different types of charging stations. In this work, we model and solve an electric vehicle routing problem (EVRP) designed for a cross-platform navigation system for individual transport. The aim is to provide users with an efficient route for their daily appointments and to reduce possible inconveniences caused by charging their EV. Based on these assumptions, we propose a multi-day model in the form of a mixed integer programming (MIP) problem that takes into account the vehicle's battery capacity and the time windows of user's appointments. The model is solved using various established metaheuristics, including tabu search (TS), adaptive large neighborhood search (ALNS), and ant colony optimization (ACO). Furthermore, the performance of the individual approaches is analyzed using generated ensembles to estimate their behavior in reality and is compared with the exact results of the Google OR-Tools solver. 6 pages, 5 figures KW - Charging stations KW - Electric vehicles KW - Metaheuristics KW - Smart cities KW - Vehicle routing Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2512.21346 ER -