IoT

This data repository comprises three distinct datasets tailored for different predictive modeling tasks. The first dataset is a synthetic dataset designed to simulate multivariate time series patterns, incorporating both linear and non-linear dependencies among input and target features. The second dataset, the Beijing Air Quality PM2.5 dataset, consists of PM2.5 measurements alongside meteorological data like temperature, humidity, and wind speed, with the objective of predicting PM2.5 concentrations.

This article presents a dataset collected from a real process control network (PCN) to facilitate deep-learning-based anomaly detection and analysis in industrial settings. The dataset aims to provide a realistic environment for researchers to develop, test, and benchmark anomaly detection models without the risk associated with experimenting on live systems. It reflects raw process data from a gas processing plant, offering coverage of critical parameters vital for system performance, safety, and process optimization.

This dataset contains inertial measurements data (accelerometer, gyroscope and magnetometer), recorded as a part of DoorINet research. Our dataset was recorded using two types of IMUs: Memsense MS-IMU3025 [32] and Movella Xsens DOT [33]. The Memsense MS-IMU3025 was used to generate the ground-truth (GT) readings. This IMU has a gyroscope bias instability of 0.8°/h over the axis of interest Z and was recorded at 250Hz. The Movella Xsens DOT IMUs were used as units under test. It has a gyroscope bias instability of 10°/h over the axis of interest Z and was recorded at 120Hz.

Our released weight dataset for fusion results in edge-cloud collaborative inference contains the corresponding weighted summation weights under 50,000 edge-cloud collaborative DNN inference tasks, listing the five heterogeneous NVIDIA edge devices they use (NVIDIA Jetson Nano, TX2, NX, Orin NX, and AGX Orin), computing power (1.9~275TOPS), DNN model type (EfficientNet-B0, ViT-B16), and network bandwidth (0.5~8Mbps).