One of the most relevant gains of pure 5 G mobile communication, i.e., Stand Alone (SA) architecture is throughput, as it enabled the addition of new features, including Voice over New Radio (VoNR) that is one of the IP Multimedia Subsystem (IMS) features, which also has other multimedia services, such as Voice over Long Term Evolution (VoLTE) and Voice Over WiFi (Vo-WiFi). Analyzing the energy consumption in mobile devices that support the Fifth Generation (5G) in the SA architecture plays an essential role in developing these devices, which commonly have limited hardware resources. This research aimed to evaluate the impacts of battery consumption during the attachment procedure leading up to VoNR call sessions across different 5 G frequency bands. The frequency band that consumed the least energy was n20.
Tiny Machine Learning (TinyML) enables on-device inference on low-power microcontrollers, but a single optimised model often cannot meet real-time and energy constraints under variable workloads. This paper presents a simulation-based approach to dynamic model or ensemble selection, where a device switches between pre-profiled models at run time to balance latency, accuracy and energy consumption. We introduce a configurable simulator that evaluates selection strategies without hardware experiments. The simulator models a real TinyML system. It supports synchronous and asynchronous samples patterns, tracks energy consumption using a simplified current-based model, and reports metrics including operating time, number of inferences, dropped samples, and weighted accuracy. The tool provides a repeatable and customizable environment for testing resource-aware inference strategies in TinyML systems. The simulator is open source and intended to aid the development of adaptive TinyML applications where responsiveness and energy efficiency must be carefully balanced.
With the widespread adoption of IoT devices, the need for enhanced security has become increasingly important. In Japan, the JC-STAR (Japanese Cyber-Security Technical Assessment Requirements) has been introduced, but the evaluation process remains labor-intensive and highly depends on individual expertise. This paper proposes a method for automating JC-STAR document assessment using Large Language Models (LLMs). Within a Retrieval-Augmented Generation (RAG) framework, the approach incorporates PDF-toMarkdown conversion and two-stage weighted Reciprocal Rank Fusion (RRF), enabling accurate and comprehensive document chunk retrieval. Additionally, a text classification LLM is used to quantify the conformance level of LLM responses, automating the scoring process. Experiments show that the proposed method achieves a lower Mean Absolute Error (MAE) of 0.259 compared to other approaches, demonstrating its effectiveness.
The monitoring of the route and track environment plays an important role in automated driving. For example, it can be used as an assistance system for route monitoring in automation level Grade of Automation (GoA) 2, where the train driver is still on board. In fully automated, driverless driving at automation level GoA4, these systems finally take over environment monitoring completely independently. With the help of artificial intelligence (AI), they react automatically to risks and dangerous events on the route. To train such AI algorithms, large amounts of training data are required, which must meet high-quality standards due to their safety relevance. In this publication we present an automatic method for assuring the quality of training data, significantly reducing the manual workload and accelerating the development of these systems. We propose an open-source tool designed to detect nine common errors found in multi-sensor datasets for railway vehicles. To evaluate the performance of the framework, all detected errors were manually validated. Six issue detection methods achieved 100
This paper investigates the enhancement of energy detection (ED) techniques in cooperative wideband spectrum sensing with subbands for IoT and 5G networks, focusing on both AWGN and Rayleigh fading environments. We propose a comprehensive framework that integrates adaptive thresholding, adaptive k-out-of-N fusion rule, and diversity combining via maximal ratio combining (MRC) to improve detection reliability while maintaining energy efficiency in dynamic spectrum access (DSA) scenarios. Cooperative sensing across subbands is leveraged to address the inherent variability in spectrum occupancy and to enable efficient spectrum sharing. The framework’s performance is evaluated using analytical derivations and extensive MATLAB simulations, considering key metrics such as the probability of detection (PD), probability of false alarm (PFA). Our findings reveal that the proposed adaptive scheme consistently outperforms conventional static approaches, particularly in low-SNR Rayleigh fading environments, thus offering a robust solution for smart city applications that rely on dynamic spectrum access and reliable spectrum sharing across subbands. Finally, we highlight potential extensions of this work to include machine learningbased optimization for adaptive parameter selection in highly dynamic environments.
