Wireless Networking

The Smart Home Device Dataset consists of 5000 samples collected at an hourly interval starting from January 2022, representing consumer electronics and IoT-enabled devices in a home automation environment. Each entry is associated with a unique device ID, ensuring identification of distinct devices. The dataset captures real-time sensor readings, including temperature variations (18°C to 30°C), power consumption levels (10W to 500W), and user activity states (Active, Idle, or Sleep), which provide contextual insights into device operation.

This dataset contains 20,000 packets of wireless channel measurements collected between two simulated devices, Alice and Bob, using the Vienna 5G Link Level Simulator. The dataset captures channel state information (CSI), signal magnitude, and phase variations under four different wireless environments: Indoor Mobile (IME), Indoor Static (ISE), Outdoor Mobile (OME), and Outdoor Static (OSE), with corresponding correlation values of 0.65, 0.82, 0.66, and 0.63, respectively.

Recently, large-scale and mega low earth orbit (LEO) constellations have become the primary development trends in satellite networks.Considering the complex topology and limited processing capabilities of LEO satellites, traditional distributed routing algorithms with significant flooding overhead are not suitable for the era of mega constellations. In this context, centralized routing technology based on Software Defined Network (SDN) architecture has been widely applied.

1. Introduction

Wireless charging technology is revolutionizing electric vehicles (EVs) by offering a convenient alternative to traditional charging. This technology enhances user experience and supports the growth of autonomous EV fleets. As electrification of transportation accelerates, effective thermal management becomes crucial to address heat generation during inductive power transfer, ensuring system reliability and longevity.

This dataset includes ALVR statistics (.json) and Wireshark packet traces (.csv) for a wide range of single-user and multi-user PCVR configurations. Our configurations are based on ALVR and SteamVR. For each user, we stream a video game from a wired server (desktop or laptop) to a wireless head-mounted display (Meta Quest 2 or 3) over Wi-Fi. In each case, the data was captured on the server.

Introduction

As electric vehicles (EVs) gain popularity, the demand for efficient and convenient charging solutions has surged. Inductive Power Transfer (IPT) technology offers a promising solution, allowing for wireless charging without the need for physical connections. Among the various advancements in this field, the development of self-aligning capability in IPT pads stands out.

We created this dataset to study Outdoor-to-Indoor (O2I) signal propagation using four UAV transmitters and 17,485 receivers positioned inside the building. For each receiver location and transmitter, we generated up to 25 multipath components by simulating six transmissions, six reflections, one diffraction, and diffused multipath (comprising two transmissions and one diffraction) using Remcom's Wireless InSite.

DALHOUSIE NIMS LAB ATTACK IOT DATASET 2025-1 dataset comprises of four prevalent types attacks, namely Portscan, Slowloris, Synflood, and Vulnerability Scan, on nine distinct Internet of Things (IoT) devices. These attacks are very common on the IoT eco-systems because they often serve as precursors to more sophisticated attack vectors. By analyzing attack vector traffic characteristics and IoT device responses, our dataset will aid to shed light on IoT eco-system vulnerabilities.

An extensive amount of carefully calibrated signal strength radio measurements at mmWave frequencies (28 GHz and 60 GHz) was performed across the 8089 m² seating area of Sausalito Stadium in Viña del Mar, Chile. Measurements were conducted with the seating area empty to ensure a static scenario. To properly characterize and represent the complete seating area of a typical stadium, 100 locations were carefully selected for measuring received power (mobile user locations, MU), and two different locations were chosen for the base station (BS).

This dataset supports the BWiFi framework, an intelligent method to identify optimal Wi-Fi zones in mesh networks. The home dataset, collected over one month across 36 zones, and the office dataset, collected over two months across 40 zones, systematically measure Wi-Fi quality and application performance metrics. Using clustering techniques and heuristic analysis, BWiFi evaluates zone performance to recommend optimal connectivity areas.