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<xml><records><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Adam Domański</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LIDAR Point Cloud generation for SLAM algorithm evaluation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2021</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Sensors</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/21/10/3313</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.3390/s21103313</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">21 (10)</style></volume><issue><style face="normal" font="default" size="100%">Special Issue: Advance in Sensors and Sensing Systems for Driving and Transportation: Part B</style></issue><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Adam Domański</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2022</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.nature.com/articles/s41598-022-22938-y</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.1038/s41598-022-22938-y</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Adam Domański</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building a real-time testing platform for unmanned ground vehicles with UDP Bridge</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2022</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Sensors</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/22/21/8493</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.3390/s22218493</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">22</style></volume><issue><style face="normal" font="default" size="100%">21</style></issue><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Adam Domański</style></author><author><style face="normal" font="default" size="100%">Rafał Cupek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Real-time testing of vision-based systems for AGVs with ArUco markers</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2022</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Big Data</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10020817</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/BigData55660.2022.10020817</style></electronic-resource-num><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Osaka, 17-20 December, 2022</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Marzena Halama</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visual examination of relations between known classes for deep neural network classifiers.</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style face="normal" font="default" size="100%">01/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">2023 IEEE International Conference on Big Data</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/BigData59044.2023.10386987</style></electronic-resource-num><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Sorrento, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jakub Szyguła</style></author><author><style face="normal" font="default" size="100%">Piotr Biernacki</style></author><author><style face="normal" font="default" size="100%">Dariusz Marek</style></author><author><style face="normal" font="default" size="100%">Adam Domański</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Jarosław Flak</style></author><author><style face="normal" font="default" size="100%">Dariusz Caban</style></author><author><style face="normal" font="default" size="100%">Piotr Pawlas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of web-based geo-visualization methods applied for Automated Guided Vehicle using Satellite Navigation Systems</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style face="normal" font="default" size="100%">12/2022</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">2022 IEEE International Conference on Big Data</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/BigData55660.2022.10020593</style></electronic-resource-num><pub-location><style face="normal" font="default" size="100%">Osaka, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ZigBeeNet: Decrypted ZigBee IoT Network Traffic Dataset in Smart Home Environment</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style face="normal" font="default" size="100%">11/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Applied Sciences</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/14/23/10844</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.3390/app142310844</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">14</style></volume><issue><style face="normal" font="default" size="100%">23</style></issue><section><style face="normal" font="default" size="100%">10844</style></section><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Piotr Biernacki</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Francisco Ferrão</style></author><author><style face="normal" font="default" size="100%">Shahid Mumtaz</style></author><author><style face="normal" font="default" size="100%">Fideline Tchuenbou-Magaia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial Intelligence for Electronic-based Systems in Safety Applications</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">02/2025</style></date></pub-dates></dates><urls><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/389204536_Artificial_Intelligence_for_Electronic-based_Systems_in_Safety_Applications</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.13140/RG.2.2.16865.42086</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Piotr Biernacki</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visual Encoding Method for Semantic Mapping with Federated Learning Concept</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year></dates><secondary-title><style face="normal" font="default" size="100%">MobiHoc '25: Proceedings of the Twenty-sixth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM Symposium on Mobile Ad Hoc Networking and Computing)</style></secondary-title><urls><style face="normal" font="default" size="100%">https://doi.org/10.1145/3704413.3765513</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1145/3704413.3765513</style></electronic-resource-num><pages><style face="normal" font="default" size="100%">428 - 435</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a visual encoding method for semantic mapping in indoor environments, designed to minimize redundancy in image data and support federated learning across a fleet of service robots. Our pipeline combines 2D LiDAR-based segmentation with RGB image filtering based on geometric orientation, distance, visibility, and uniqueness. The result is a compact set of representative visual samples suitable for downstream semantic tasks such as object recognition or language grounding. We evaluate our method in a Gazebo simulation using a TurtleBot platform and compare it against a naive odometry-based sampling strategy. Our approach achieves up to 57.5\% reduction in collected images while preserving scene coverage. Additionally, we demonstrate how multiple robots can collaboratively improve the visual map in a federated setup, reducing collection time and enabling model generalization across diverse environments. The proposed method offers an efficient and scalable solution for semantic mapping under bandwidth and computation constraints.&lt;/p&gt;&#13;
</style></abstract><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When LLMs Speak ZigBee: Exploring Low-Latency and Reasoning Models for Network Traffic Generation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.nature.com/articles/s41598-026-37246-y</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1038/s41598-026-37246-y</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The generation of realistic network traffic is a critical requirement for testing, simulation, and security evaluation in ZigBee-based IoT systems. In this study, we propose a novel framework that extracts sample ZigBee traffic packets and generates semantically meaningful and protocol-compliant synthetic traffic by using Large Language Models (LLMs) such as GPT-4.1 and GPT-5. Unlike traditional rule-based or statistical generators, our approach is data-driven and incorporates sample-based few-shot learning, prompt engineering, and a human-in-the-loop feedback mechanism. To evaluate the effectiveness of the proposed framework, we conduct two sets of experiments. The first focuses on generating unidirectional traffic to emulate typical device-to-hub communication, while the second extends this setup to bidirectional exchanges, capturing realistic request--response dynamics and interaction patterns. The realism of the generated traffic is assessed using a multi-dimensional evaluation framework that includes statistical similarity measures, such as Jensen--Shannon Divergence, as well as protocol compliance, semantic correctness, temporal consistency, and diversity metrics. In addition, we compare the performance of LLM-based generators with classical deep learning baselines, including recurrent neural networks (RNNs) and generative adversarial networks (GANs). We further analyze the computational cost and the impact of internal reasoning effort on traffic generation by systematically evaluating different GPT-5 reasoning configurations. Experimental results show that both GPT-4.1 and GPT-5 successfully learn the temporal and structural dependencies of ZigBee traffic and significantly outperform RNN and GAN baselines in terms of semantic correctness and long-duration generation. Across all experiments, GPT-4.1 consistently generates traffic that more closely resembles real ZigBee traffic while requiring substantially lower computational resources. These findings highlight that low-latency, non-reasoning LLMs can be particularly well suited for highly structured, protocol-constrained network traffic generation tasks, and demonstrate the potential of LLM-based approaches for realistic IoT traffic generation in research and security evaluation.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CPU Utilization Forecasting in Large-Scale Cloud Clusters Using Gradient Boosting</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">7th Polish Conference on Artificial Intelligence (PP-RAI'2026)</style></secondary-title><publisher><style face="normal" font="default" size="100%">(This work was partly supported by the National Centre for Research and Development (NCBR) and co-funded by the European Union under the European Funds for ModernEconomy(FENG)Programme(SMARTKonsorcja),projectno.FENG.01.01-IP.01-A0GV/24-00,)</style></publisher><pub-location><style face="normal" font="default" size="100%">Kraków, Poland </style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reliability-Aware LLM Reasoning: Handling Uncertainty in Robot Perception</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">10.08.2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2026)</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/11714851</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/RO-MAN72286.2026.11714851</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Robots operating in human environments must often make decisions based on perceptual information that&lt;br&gt;is uncertain, incomplete, or ambiguous. This paper proposes a reliability-aware reasoning framework that enables large&lt;br&gt;language models (LLMs) to account for perceptual uncertainty when selecting actions in human-robot interaction scenarios.&lt;br&gt;The environment is represented as a structured scene composed of detected objects enriched with confidence estimates, attribute reliability, and spatial uncertainty information. Using this representation, the LLM evaluates candidate objects through a reliability scoring mechanism that integrates multiple sources of perceptual evidence and supports uncertainty-aware decision making.The proposed approach is evaluated using perception episodes with controlled levels of uncertainty and compared with a baseline LLM-based matching strategy that ignores perceptual&lt;br&gt;reliability. Experimental results show that incorporating uncertainty-aware reasoning substantially improves decision&lt;br&gt;robustness under medium and high uncertainty conditions while reducing safety-critical behaviors caused by overconfident&lt;br&gt;autonomous decisions.&lt;/p&gt;</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Kitakyushu, Japan</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Risk-Aware Response Refinement for Safer LLM-Powered Socially Assistive Robots in Elderly Care</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">10.08.2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2026)</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/11714788</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/RO-MAN72286.2026.11714788</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Safe and reliable human–robot interaction remains a challenge, especially for Large Language Model (LLM)-powered socially assistive robots in elderly care. This paper proposes a risk-aware response framework that explicitly models the safety implications of user queries. &#160;We introduce a Query Risk Assessment Module (QRAM), which computes a Query Risk Score (QRS) using structured semantic indicators to classify user inputs into different risk levels. Based on this, we develop two strategies: risk-aware response refinement and risk-aware response generation. To assess safety, we introduce the Risk-Aware Response Safety Score (RRSS), a metric that captures both the presence of risk-bearing language and the absence of necessary safety-critical guidance. Experimental results across multiple LLMs demonstrate that incorporating query-level risk awareness reduces response-level risk, with both strategies outperforming raw responses.&lt;/p&gt;</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Kitakyushu, Japan</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Sławomir Nowak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)</style></secondary-title><pub-location><style face="normal" font="default" size="100%">Naples, Italy</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">10/05/2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">ACM International Conference on Multimodal Interaction (ICMI)</style></secondary-title><urls><style face="normal" font="default" size="100%">https://dl.acm.org/doi/10.1145/3776591.3833865</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1145/3776591.3833865</style></electronic-resource-num><pub-location><style face="normal" font="default" size="100%">Naples, Italy</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Internet of Things Journal</style></secondary-title><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv </style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2606.22496 </style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.48550/ARXIV.2606.22496 </style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AIRWISE: Environmental Sensor and LLM Query Dataset for Edge AI-Based Air-Quality and Thermal-Comfort Analytics</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><urls><style face="normal" font="default" size="100%">https://zenodo.org/doi/10.5281/zenodo.20783874 </style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.5281/ZENODO.20783874 </style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;AIRWISE is an open benchmark dataset for edge AI, IoT environmental monitoring, and large language model (LLM) evaluation on real sensor data. It combines indoor air-quality and thermal-comfort measurements, outdoor air-quality and meteorological time series, and a labeled binary question-answering benchmark for threshold-aware reasoning and anomaly detection. The dataset supports the study “Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing”:&#160;https://doi.org/10.48550/arXiv.2606.22496&lt;br&gt;AIRWISE provides three core resources:&lt;br&gt;Indoor environmental sensor dataset (BME680, 1-minute resolution). 146,000+ minute-averaged readings from Raspberry Pi + Bosch BME680 nodes in three real micro-environments (office, kitchen, hallway) at Tampere, Finland (2025-11-14 to 2025-12-18): temperature, relative humidity, barometric pressure, gas resistance, indoor air quality (IAQ) proxy, z-score anomaly detectors, and limit-annotated long-format tables with ground-truth anomaly flags.&lt;br&gt;Outdoor air-quality and meteorological time series (hourly, full-year 2023). Helsinki (Finland), Katowice and Warsaw (Poland): NO₂, CO, O₃, PM₂.₅, temperature, and relative humidity assembled from national monitoring networks (FMI, GIOŚ, IMGW-PIB), as cleaned single-series city tables (≈8,760 hourly records per city) and limit-annotated long-format tables with anomaly flags.&lt;br&gt;Binary LLM query benchmark (1,440 labeled yes/no questions). 240 natural-language questions per site across six sites, balanced by task type (80 air quality, 80 thermal comfort, 80 joint environmental condition), each with ground-truth answer, violation label, offending factors, and rationale — ready for benchmarking local/edge and cloud LLMs on sensor-grounded reasoning.&lt;br&gt;Also included: reference air-quality and thermal-comfort limits (EU air-quality standards and Finnish/Polish national guidelines); the unmodified institutional source files for the outdoor component (FMI exports, GIOŚ archive workbooks and station metadata, IMGW-PIB synoptic tables); the complete dataset-generation scripts covering every pipeline step (raw sources → cleaned tables → limit-annotated data → LLM queries) with a requirements.txt for a verified minimal Python environment; SHA-256 checksums; and a dependency-free validation script. Schemas, exact row counts, coverage, and usage examples are documented in README.md and MANIFEST.md.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge-Graph-Grounded Multimodal Explanations for Trustworthy Time-Series Forecasting in DevOps Systems</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference (ISWC)</style></secondary-title><pub-location><style face="normal" font="default" size="100%">Bari, Italy</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Why the Robot Said No: Knowledge-Graph-Grounded Explanations for LLM-Based Robot Planning</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference (ISWC)</style></secondary-title><pub-location><style face="normal" font="default" size="100%">Bari, Italy</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Grounding LLM-Generated Network Traffic in Semantics: A Knowledge-Graph Framework for Detecting and Explaining Protocol Hallucinations</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">ACM International Conference on Information and Knowledge Management (CIKM)</style></secondary-title></record></records></xml>
