This paper explores the applications of the 45 MHz U-NII-4 band in vehicle-to-everything (V2X) communication system, a technology adopted (or being adopted) by numerous countries to facilitate safety warning applications and mitigate collision risks. However, the operational efficiency of V2X systems can be undermined by intentional and unintentional interference provoked by the increasing user base in adjacent bands and potential malicious entities in the V2X operating band.
The integration of Large Language Models (LLMs) into wireless communications for channel state information (CSI) prediction introduces transformative capabilities but also exposes critical security vulnerabilities, particularly backdoor attacks. This paper investigates how adversaries exploit the openness of wireless propagation where signals are inherently susceptible to eavesdropping and adversarial interference, and the black-box nature of neural networks to inject stealthy triggers (e.g., Gaussian white, narrowband, or impulse interference) into online training samples.
This dataset is associated with the paper entitled "User-Centric Caching in Personal Vehicles: MyCar-MyCache". It tracks 3,463 app sessions from 5 users over 14 days in a residential wireless local area network. Seven distinct apps appear with App_A being most popular (678 sessions). Usage peaks midday (12pm: 210 sessions) and is lowest late evening (11pm: 89 sessions). Average session duration is 30 minutes with 50.2 MB data consumption per session. Daily activity remains consistent (234-266 sessions) with no significant day-of-week patterns.
This dataset contains 60,000 annotated records modeling UAV-based and IoT sensor-driven agriculture environments. Each record includes UAV imaging data (NDVI, NDRE, RGB damage score), IoT sensor values (NPK, pH, moisture, temperature, humidity), semantic labels (NDI, PDI), and metadata for energy consumption, latency, and service migration. It is designed for validating Digital Twin frameworks, semantic communication models, and Federated Deep Reinforcement Learning (FDRL) in precision farming.
<p>This dataset contains simulation results generated in OptiSystem for an 18‐tupling optical communication system. The parameters include optical source settings, modulator configurations, and a range of power and signal quality metrics. Key performance indicators—such as total power penalty (TPP), signal power penalty (SPP), and RMS jitter—are provided for each set of simulation inputs. The data are intended to facilitate reproducible research and to enable further analysis of high‐order frequency multiplication in optical networks.
This dataset provides packet traces captured in a realistic 5G Vehicle-to-Everything (5G-V2X) environment, encompassing both legitimate vehicular communications and Distributed Denial of Service (DDoS) attacks. By deploying four user equipments (UEs) under multiple attacker configurations, the collected captures reflect various DDoS types (TCP SYN, UDP, and mixed) and reveal their impact on 5G-V2X networks. The dataset is further enriched with Argus files and CSV feature tables, facilitating data-driven approaches such as Machine Learning (ML)-based detection agents.
This file contains the source codes of the proposed end-nodes for a Wireless Sensor Network (WSN) for hydrometeorological monitoring in the article entitled "Hydrometeorological Monitoring using Wireless Sensor Networks". These codes were developed to perform LoRaWAN communication range tests and to test two distinct sensor nodes with different functionalities: a meteorological sensor node and a hydrological sensor node.
In recent years, with the rapid development of Internet of Things technology, the application and popularization of intelligent sports equipment in residents’ lives have become extensive. As a sport suitable for all ages, badminton is deeply loved by the masses. This paper designs a smart badminton racket to help students improve their badminton skills by providing real-time training data and three-dimensional posture information.
IPv6 Hitlist 1M is an open-source dataset provided by Gasser et al., aimed at creating a comprehensive list of active IPv6 addresses, incorporating results from multiple public address sets and updating daily. We selected the probing results from May 4, 2024, to May 10, 2024. This dataset contains a total of 24.5 million addresses, from which we selected the top 1 million addresses as IPv6 Hitlist 1M.
IPv6 Hitlist 1M is an open-source dataset provided by Gasser et al., aimed at creating a comprehensive list of active IPv6 addresses, incorporating results from multiple public address sets and updating daily. We selected the probing results from May 4, 2024, to May 10, 2024. This dataset contains a total of 24.5 million addresses, from which we selected the top 1 million addresses as IPv6 Hitlist 1M.
