TY - CHAP A1 - Thelen, Simon A1 - Volbert, Klaus A1 - Nunes, Danilo Weber T1 - A Survey on Algorithmic Problems in Wireless Systems T2 - Proceedings of the 12th International Conference on Sensor Networks (SENSORNETS), Vol 1, Feb 23, 2023 - Feb 24, 2023, Lisbon, Portugal N2 - Considering the ongoing growth of Wireless Sensor Networks (WSNs) and the challenges they pose due to their hardware limitations as well as the intrinsic complexity of their interactions, specialized algorithms have the potential to help solving these challenges. We present a survey on recent developments regarding algorithmic problems which have applications in wireless systems and WSNs in particular. Focusing on the intersection between WSNs and algorithms, we give an overview of recent results inside this intersection, concerning topics such as routing, interference minimization, latency reduction, localization among others. Progress on solving these problems could be potentially beneficial for the industry as a whole by increasing network throughput, reducing latency or making systems more energy-efficient. We summarize and structure these recent developments and list interesting open problems to be investigated in future works. KW - Algorithms KW - WSNs KW - Survey KW - Network Construction KW - Routing KW - Interference KW - Localization KW - Charging KW - Latency Y1 - 2023 SN - 978-989-758-635-4 U6 - https://doi.org/10.5220/0011791200003399 SP - 101 EP - 111 PB - SCITEPRESS - Science and Technology Publications ER - TY - INPR A1 - Thelen, Simon A1 - Safi, Hila A1 - Mauerer, Wolfgang T1 - Approximating under the Influence of Quantum Noise and Compute Power T2 - Proceedings of WIHPQC@IEEE QCE N2 - The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles challenge concrete benefits now and in the foreseeable future: Imperfections quickly degrade algorithmic performance below practical utility; overheads arising from alternating between classical and quantum primitives can counter any advantage; and the choice of parameters or algorithmic variant can substantially influence runtime and result quality. Selecting the optimal combination is a non-trivial issue, as it not only depends on user requirements, but also on details of the hardware and software stack. Appropriate automation can lift the burden of choosing optimal combinations for end-users: They should not be required to understand technicalities like differences between QAOA variants, required number of QAOA layers, or necessary measurement samples. Yet, they should receive best-possible satisfaction of their non-functional requirements, be it performance or other. We determine factors that affect solution quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based simulations targeting three widely studied optimisation problems. Our simulations consider ideal quantum computation, and a continuum of scenarios troubled by realistic imperfections. Our quantitative results, accompanied by a comprehensive reproduction package, show strong differences between QAOA variants that can be pinpointed to narrow and specific effects. We identify influential co-variables and relevant non-functional quality goals that, we argue, mark the relevant ingredients for designing appropriate software engineering abstraction mechanisms and automated tool-chains for devising quantum solutions from high-level problem specifications. Y1 - 2024 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 - Thelen, Simon A1 - Mauerer, Wolfgang T1 - Predict and Conquer: Navigating Algorithm Trade-Offs with Quantum Design Automation T2 - 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque N2 - Combining quantum computers with classical compute power has become a standard means for developing algorithms and heuristics that are, eventually, supposed to beat any purely classical alternatives. While in-principle advantages for solution quality or runtime are expected for increasingly many approaches, substantial challenges remain: Non-functional properties like runtime or solution quality of many suggested approaches are not yet fully understood, and need to be explored empirically. This, in turn, makes it unclear which approach is best suited for a given problem. Accurately predicting behaviour and properties of quantum-classical algorithms opens possibilities for software abstraction layers, which in turn can automate decisionmaking for algorithm selection and parametrisation. While such techniques find frequent use in classical high-performance computing, they are still mostly absent from quantum software toolchains. In this paper, we present a methodology (accompanied by a reproducible reference implementation) to perform algorithm selection based on desirable non-functional requirements. This greatly simplifies decision-making processes for end users. Based on meta-information annotations at the source code level, our framework traces key characteristics of quantum-classical heuristics and algorithms, and uses this information to predict the most suitable approach and its parameters for given computational challenges and their non-functional requirements. As combinatorial optimisation is a very extensively studied aspect of quantumclassical systems, we perform a comprehensive case study based on numerical simulations of algorithmic approaches to implement and validate our ideas. We develop statistical models to quantify the influence of various factors on non-functional properties, and establish predictions for optimal algorithmic choices without manual user effort. We argue that our methodology generalises to problem classes beyond combinatorial optimisation, such as Hamiltonian optimisation, and lays a foundation for integrated software layers for quantum design automation. KW - Design automation KW - Heuristic algorithms KW - Optimization KW - Prediction algorithms KW - Quantum Computing KW - Runtime KW - Software KW - Software algorithms KW - Source coding KW - Standards Y1 - 2025 U6 - https://doi.org/10.1109/QCE65121.2025.00071 SP - 591 EP - 602 PB - IEEE CY - Los Alamitos, USA ER -