IoT

ImgFi converts wifi channel state information into images, improving feature extraction and achieving 99.5% accuracy in human activity recognition using only three layers of convolution. In addition to the self-test dataset, three publicly available high-quality datasets, WiAR, SAR and Widar3.0, are used. WiAR collects 16 activity-reflected WiFi signals; SAR collects WiFi signals in response to 6 actions performed by 9 volunteers over 6 days, while Widar3.0 collects 6 action signals from 5 volunteers at different locations and antenna orientations.
- Categories:

This dataset presents the measurement results for the evaluation study on the performance of an inductor-based and a switched capacitor-based energy harvesting boost converter PMICs by their charging efficiencies when connected to photovoltaic cells and Li-ion batteries under indoor lighting conditions.
- Categories:

Pictures and simulation file for the Zero dynamics anomaly behaviour paper is attached
- Categories:

Pictures and simulation file for the Zero dynamics anomaly behaviour paper is attached
- Categories:

Pictures and simulation file for the Zero dynamics anomaly behaviour paper is attached
- Categories:
This project builds a length-versatile and noise-robust LoRa radio frequency fingerprint identification (RFFI) system. The LoRa signals are collected from 10 commercial-off-the-shelf LoRa devices, with the spreading factor (SF) set to 7, 8, 9, respectively. The packet preamble part and device labels are provided.
- Categories:

Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR).
- Categories:
Industries transition to the Industry 4.0 paradigm requires solutions based on devices attached to machines that allow monitoring and control of industrial equipment. Monitoring is essential to ensure devices' proper operation against different aggressions. We propose a novel approach to detect and classify faults, that are typical in these devices, based on machine learning techniques that use as features the energy, the processing, and the time consumed by device main application functionality.
- Categories:

The dataset used in the study consists of different IoT network traffic data files each IoT traffic data has files containing benign, i.e. normal network traffic data, and malicious traffic data related to the most common IoT botnet attacks which are known as the Mirai botnet.
- Categories: