IoT

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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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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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.

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In traditional schemes, obtaining a robust topology requires high connectivity. We chose random undirected and directed network topologies for comparison to showcase the

advantages of entropy and connectivity.

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To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area.

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The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries.

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Cars, mobile phones, and smart home devices already provide automatic speech recognition (ASR) by default. However, human machine interfaces (HMI) in industrial settings, as opposed to consumer settings, operate under different conditions and thus, present different design challenges. Voice control, arguably the most natural form of communication, has the potential to shorten complex command sequences and menu structures in order to directly execute a final command.

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This video shows the working process of RF-EH WSN and the maximum energy collection distance (14m).

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The human gait is unique and so is the impact of a walking human on the propagation of wireless signals within a wireless network. Using appropriate pattern recognition techniques, a person can thus be identified just from a time series of Received Signal Strength (RSS) measurements. This dataset holds bidirectional RSS measurements recorded within a mesh network of four Bluetooth sensor devices. During the measurements, a total of 14 subjects walked individually through the setup. A total of more than 10,000 recordings are provided.

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The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This feature is usually referred to as Bluetooth Direction Finding. Besides localization, this new technology can be used to implement a radar system just using commodity Bluetooth chipsets. This dataset holds measurements recorded using a demonstration setup for such kind of radar system. Along with this dataset, the experimental setup, the used sensor devices, and the structure of the provided data files are described in detail.

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