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RT-IoT2022
The RT-IoT2022, a proprietary dataset derived from a real-time IoT infrastructure, is introduced as a comprehensive resource integrating a diverse range of IoT devices and sophisticated network attack methodologies. This dataset encompasses both normal and adversarial network behaviours, providing a general representation of real-world scenarios. Incorporating data from IoT devices such as ThingSpeak-LED, Wipro-Bulb, and MQTT-Temp, as well as simulated attack scenarios involving Brute-Force SSH attacks, DDoS attacks using Hping and Slowloris, and Nmap patterns, RT-IoT2022 offers a detailed perspective on the complex nature of network traffic. The bidirectional attributes of network traffic are meticulously captured using the Zeek network monitoring tool and the Flowmeter plugin. Researchers can leverage the RT-IoT2022 dataset to advance the capabilities of Intrusion Detection Systems (IDS), fostering the development of robust and adaptive security solutions for real-time IoT networks.
Bosch CNC Machining Dataset
Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at: https://github.com/boschresearch/CNC_Machining.
Traffic Flow Forecasting
The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations.
Physical Therapy Exercises
This dataset contains wearable inertial and magnetic sensor data during the execution of physical therapy exercises. There are eight types of physical therapy exercises, each of which has three execution types (correct, fast, and low-amplitude). Each execution type of each type of exercise was performed multiple times by five subjects. The subjects wore five MTx sensor units manufactured by XSens. Each unit contains three tri-axial sensors: an accelerometer, a gyroscope, and a magnetometer, sampled at 25 Hz.
Dataset based on UWB for Clinical Establishments
The authors come forth with a data set acquired from an intelligent surveillance system based on the bleeding edge technology – Ultra wide band technology. The intelligent surveillance system is proposed to prefect the movement of patients in and out of hospitals and other clinical establishments. The raw data is amassed from UWB anchors and tags affixed in the clinical arena using a wearable tag. The chronophagous behaviour of following up on the records of patients with respect to their arrival and departure manually is abhorred using the proposed surveillance system. The data described in the manuscript is a result of the system implemented in an area of 12.5m X 16.5m inside a hospital premises.
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