IoT
This LoRa-RFFI project builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techniques. The RF signals are collected from 60 commercial-off-the-shelf LoRa devices. The packet preamble part and device labels are provided. The dataset consists of 19 sub-datasets and please refer to the README document for more detailed collection settings for all the sub-datasets.
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Network Data
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# RSS data from smartwatch for Contact Tracing
This dataset was collected for the purpose to understand the proximity between any two smartwatches worn by human.
We used the Google's Wear OS based smartwatch, powered by a Qualcomm Snapdragon Wear 3100 processor, from Fossil sport to collect the data.
The smartwatch is powered by a Qualcomm Snapdragon Wear 3100 processor and has an internal memory of up to 1GB.
Two volunteers were required to wear the smartwatch on different hand and stand at a certain distance from each other.
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729 Views
A Dataset Bundle for Building Automation and Control Systems
useful for Security Analysis and to study the normal operation of these systems
This document describes a dataset bundle with diverse types of attacks, and also a not poisoned dataset. The capture was obtained in a real house with a complete Building Automation and Control System (BACS). This document describes the several included datasets and how their data can be employed in security analysis of KNX based building Automation.
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This is the First Arabic voice Commands Dataset to provide personalized control of devices at smart homes for elder persons and persons with disabilities. The dataset contains 12 speakers, each saying 36 different phrases or words in Arabic language. The goal of this dataset is to use it in an Arabic smart home system to control home devices through voice. Participants were asked to say each phrase multiple times. The phrases to record were presented in a random order.
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866 Views
The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.
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In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer.
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With the current footprint of the embedded IoT industries, there is a huge requirement to understand and analyze the existing IoT boards to provide the future IoT board manufacturers with a direction of research and development.
This datasheet contains the comparative architectural survey of 59 computing boards from the rapid prototyping industries. This dataset was collected directly from the manufacturers’ datasheet. The motivation to build this dataset is to analyze the current advancements in the IoT industries to reflect the future architectural designs of various boards.
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211 Views
Power system state estimation (PSSE) plays a vital role in stable operation of modern smart grids, while it is vulnerable to cyber attacks. False data injection attacks (FDIA), one of the most common cyber attacks, can tamper with measurement data and bypass the bad data detection (BDD) mechanism, leading to incorrect PSSE results.
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1037 Views
This dataset contains 22 blocks which are designed based on Scratch programming tool for children. These blocks are flagged by HCI experts and primary schooll teachers as "Usefule" and "Understandable".
The usefulness of the blocks are measured based on a task list provided in a brainstorming session with children of age 8-12.
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130 Views