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.

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.

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.

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.