IoT

Generalized cross-domain sensing is a crucial step in driving the Internet of Everything. This dataset provides CSI information of Wi-Fi for different recognition tasks (gesture vs. gait) as well as DFS and (Absolute Distance Profile) ADP for researchers to validate the ADP. The ADP was tested on both the CNN-RNN networks that we utilized with parameter settings comparable to Widar 3.0, trained for 100 cycles. Then, we attach the confusion matrix for different tasks, which has been shown in the folder of the same level as the dataset, and you can refer to it for your reference.
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The data is collected from the deployed IoT sensor node at a pilot farm in Narrabri, Australia. The dataset includes information about soil characteristics such as soil moisture and soil temperature at 20-40-60 cm depth. The sensor node also provides information about environmental influencers, which are critical in constructing machine learning models to predict Evapotranspiration in diverse soil and environmental conditions.
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The necessity for strong security measures to fend off cyberattacks has increased due to the growing use of Industrial Internet of Things (IIoT) technologies. This research introduces IoTForge Pro, a comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns.
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With the accelerating pace of population aging, the urgency and necessity for elderly individuals to control smart home systems have become increasingly evident. Smart homes not only enhance the independence of older adults, enabling them to complete daily activities more conveniently, but also ensure safety through health monitoring and emergency alert systems, thereby reducing the caregiving burden on families and society.
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A novel ultra-low-voltage (ULV) Dual-EdgeTriggered (DET) flip-flop based on the True-Single-PhaseClocking (TSPC) scheme is presented in this paper. Unlike Single-Edge-Triggering (SET), Dual-Edge-Triggering has the advantage of operating at the half-clock rate of the SET clock. We exploit the TSPC principle to achieve the best energy-efficient figures by reducing the overall clock load (only to 8 transistors) and register power while providing fully static, contention-free functionality to satisfy ULV operation.
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MobRFFI is a WiFi device fingerprinting and re-identification dataset collected in the Orbit testbed facility in July and April 2024. The dataset contains raw IQ samples of WiFi transmissions captured at 25 Msps on channel 11 (2462 MHz) in the 2.4 GHz band, using Ettus Research N210r4 USRPs as receivers and a set of WiFi nodes equipped with Atheros AR5212 chipsets as transmitters. The data collection spans two days (July 19 and August 8, 2024) and includes 12,068 capture files totaling 5.7 TB of data.
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The Machine Failure Predictions Dataset (D_2) is a real-world dataset sourced from Kaggle, containing 10,000 records and 14 features pertinent to IIoT device performance and health status. The binary target feature, 'failure', indicates whether a device is functioning (0) or has failed (1). Predictor variables include telemetry readings and categorical features related to device operation and environment. Data preprocessing included aggregating features related to failure types and removing non-informative features such as Product ID.
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This dataset provides realistic Internet of Things (IoT) traffic time-series data generated using the novel Tiered Markov-Modulated Stochastic Process (TMMSP) framework. The dataset captures the unique temporal dynamics and stochastic characteristics of three distinct IoT applications: smart city, eHealth, and smart factory systems. Each application's traffic pattern reflects real-world behaviors including human-machine correlation (HMC), sudden data bursts, and application-specific seasonality patterns.
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With the rapid development of IoT technology, IoT-enabled systems, represented by smart homes, are becoming ubiquitous. In order to support personalized user requirements, such systems appeal to the end-user programming paradigm. This paradigm allows end-users to describe their requirements using TAP (Trigger-Action Programming) rules, which can be deployed on demand. However, writing TAP rules is error-prone and end-users are often unaware of the actual effects of the rules they write, given the context-sensitive nature of these effects.
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Now days, everything in the world is almost becoming automated. Technology has changed the view through which earlier we used to look at the objects. In the 21st century, we are now opting for more easy options for accessing the technology and day to day objects. The best example is banking where in previous days; the account holder had to go far to the bank and stand in the queue and then transfer or cash the money. But same is now possible on even a basic cell phone where you can transfer the cash, pay the bills very easily not more than five minutes.
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