IoT

Cattle health monitoring is essential in the modern world, because of the high demand for dairy products. Regular monitoring is essential to extend the lifecycle of cattle and maintain the quality of dairy products. Unfortunately, Observing the health of cattle regularly is difficult in large farms where workers do not have enough time to do so. This paper described IoT devices such as skin temperature, heart rate, and motion sensor. Using this device, you can monitor cattle’s heart rate, activity level, heat stress, the surrounding temperature, and sleep tracking.

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1812 Views

In this investigation, the researchers have used a commercially available millimeter-wave (MMW) radar to collect data and assess the performance of deep learning algorithms in distinguishing different objects. The research looks at how varied ambiance factors, such as height, distance, and lighting, affect object recognition ability in both static and dynamic stages of the radar.

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498 Views

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

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1952 Views

Smart home automation is part of the Internet of Things that enables house remote control via the use of smart devices, sensors, and actuators. Despite its convenience, vulnerabilities in smart home devices provide attackers with an opportunity to break into the smart home infrastructure without permission. In fact, millions of Z-Wave smart home legacy devices are vulnerable to wireless injection attacks due to the lack of encryption support and the lack of firmware updates.

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175 Views

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.

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1311 Views

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.

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106 Views

  

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.

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1282 Views

This dataset contains energy consumption of a smart home appliance located in Cheonan, South Korea. It was collected from an area called buseong 1-dong. The attributes of this dataset are as follows:

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538 Views

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).

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1164 Views

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.

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685 Views

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