Internet of Things (IoT)
This ZIP file contains two distinct datasets collected over a 14-day period. The first dataset consists of real-world smart home data, providing detailed logs from six devices: a Plug Fan, Plug PC, Humidity Sensor, Presence Sensor, Light Bulb, and Window Opening Sensor. The data includes device interactions and environmental conditions such as temperature, humidity, and presence. The second dataset is generated by a smart home simulator for the same period, offering simulated device interactions and environmental variables.
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This dataset comprises a range of features, including time slots, device IDs, geographic coordinates (x, y), energy consumption, uplink history, emergency status, QoS pool identifiers, data flags, resource IDs, and data sizes. The device locations are modeled using a Poisson distribution with a spread of \(100\) meters within a \(500 \times 500\) meter area. The uplink history, QoS pool assignments, and data flags are derived from the probabilities of data availability and priority values.
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We investigated the long term functionality of the designed PMCS in a practical use case where we monitored plant growth of classical horticulture Dianthus flowers (Dianthus carthusianorum) under the effect of plastic mulching over a period of 4 months. This use case represents a common phenological monitoring case that can be used in agricultural studies. For this, we integrated our PMCS in an embedded vision camera equipped with the openMV H7 Plus board in a waterproof housing [26]. The system shown in Fig.
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Real-time detection of X/gamma radiation dose rates holds particular significance in nuclear science research. In this study, we developed a portable X/gamma-ray survey meter for large-scale distributed real-time monitoring of ambient dose equivalent rates in the surrounding environment. This innovative device uses a silicon photomultiplier coupled with a CsI(Tl) scintillator and can connect to an Internet of Things (IoT) network.
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This dataset contains one month of the binary activity of the 4060 urban IoT nodes. Each record in the dataset presents the node ID, the time stamp, the location of the IoT node in latitude and longitude, and also the binary activity of the IoT node. The main purpose of this dataset is to be used as part of distributed denial of service (DDoS) attack research.
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Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.
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Datasets as described in the research paper "Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT Applications".
There are two main dataset provided here, firstly is the data relating to the initial training of the machine learning module for both normal and malicious traffic, these are in binary visulisation format, compresed into the document traffic-dataset.zip.
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The rise of the Internet of Things (IoT) has opened new research lines that focus on applying IoT applications to domains further beyond basic user-grade applications, such as Industry or Healthcare. These domains demand a very high Quality of Service (QoS), mainly a very short response time. In order to meet these demands, some works are evaluating how to modularize and deploy IoT applications in different nodes of the infrastructure (edge, fog, cloud), as well as how to place the network controllers, since these decisions affect the response time of the application.
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