IoT
Measurement campaign was performed across the entire growing season of 2023. Seven Arduino+LoRaWAN sensors were measuring soil moisture, temperature and pH, as well as solar irradiance in the radius of about 35 km around Gdansk, Poland. Raw data were being collected with a nomadic gateway aboard an UAV and transferred to the cloud for analysis (package part A). Based on the physics informed analysis of the underlying measurement processes anomalies were classified and parametrized.
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This dataset includes channel delay data for 5G and TSN networks.The 5G and TSN channel delay dataset includes a training set and a test set, with 600 sets of data in the training set and 200 sets of data in the test set, which are used for channel model prediction. The data in these datasets are real, collected in real-time from the running 5G-TSN system using network testers and data packet capture tools.
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To cover a large number of IoT use cases, we test our proposed framework against eight different application types with diverse task objectives, ranging from sensing to cryptography. For notational ease, the developed firmware are represented as Fi, with i = 1, 2, . . . , 8. The specific operations coded in these firmware are discussed below:
F1: A given input data block is first encrypted and then decrypted to retrieve the original data. The AES128 algorithm is used in this process and both encryption and decryption is performed in different functions, executed iteratively.
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This dataset is generated for the purpose of developing and testing attestation techniques for IoT devices. The dataset consists of RAM traces for eight different firmwares including traces for running the legitimate firmware as well as tampered versions of the firmwares. we upload the firmware onto the IoT device and allow it to operate for a predefined time period of 300 seconds. Throughout the device's normal operation, we utilize the gateway node to collect numerous RAM trace samples, each comprising 2048 bytes, with randomized intervals between consecutive samples.
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A dataset characterizing the parameters of a stepped piezoelectric cantilever beam using various geometric specifications is introduced. The dataset also shows the electric potential generated at each layer when exposed to a specific excitation frequency. The primary application of the cantilever is in ambient vibration energy harvesting, where the piezoelectric cantilever beam plays a crucial role in converting mechanical vibrations into electrical energy.
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AirIoT is a temporal dataset of air pollution concentration values measured for almost three years in Hyderabad, India. In AirIoT, a dense network of IoT-based PM monitoring devices equipped with low-cost sensors was deployed. The research focuses on two primary aspects: measurement and modelling. The team developed, calibrated, and deployed 50 IoT-based PM monitoring devices throughout Hyderabad, India, covering urban, semi-urban, and green areas.
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executing the coding and topological experiments while simulating the automation pyramid model we constructed with Ifogsim. the pyramid's layers, the changes that occur at each level, and the connections between clouds and communication at every level.executing the coding and topological experiments while simulating the automation pyramid model we constructed with Ifogsim.
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This data reflects the prevalence and adoption of smart devices. The experimental setup to generate the IDSIoT2024 dataset is based on an IoT network configuration consisting of seven smart devices, each contributing to a diverse representation of IoT devices. These include a smartwatch, smartphone, surveillance camera, smart vacuum and mop robot, laptop, smart TV, and smart light. Among these, the laptop serves a dual purpose within the network.
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¢This study delves into the connections between green ELT, DEIB, virtual reality, mediation, life skills, and task-based teaching, learning, and assessment in the context of sustainable and inclusive education. The study emphasizes the significance of incorporating ecological concepts into language instruction, advocating for diversity, fairness, and inclusivity in learning environments, and using virtual reality technology to augment language acquisition.
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The development of metaverses and virtual worlds on various platforms, including mobile devices, has led to the growth of applications in virtual reality (VR) and augmented reality (AR) in recent years. This application growth is paralleled by a growth of interest in analyzing and understanding AR/VR applications from security and performance standpoints. Despite this growing interest, benchmark datasets are lacking to facilitate this research pursuit.
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