IoT

Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data.

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.

This paper presents an innovative Internet of Things (IoT) system that integrates gas sensors and a custom Convolutional Neural Network (CNN) to classify the freshness and species of beef and mutton in real time. The CNN, trained on 9,928 images, achieved 99% accuracy, outperforming models like ResNet-50, SVM, and KNN. The system uses three gas sensors (MQ135, MQ4, MQ136) to detect gases such as ammonia, methane, and hydrogen sulfide, which indicate meat spoilage.

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.

The accuracy of temperature & humidity prediction directly affects indoor environmental control, and current predictions mainly focus on time modeling, lacking spatiotemporal modeling based prediction for distributed sensors installed in buildings. Therefore, this article proposes an indoor temperature and humidity prediction method based on spatiotemporal modeling and transfer learning of informer. A IOT platform is designed with 8 temperature & humidity integrated sensors in a public building.

This dataset presents a comprehensive video collection of Internet of Things (IoT) products, encompassing both market successes and failures. Its primary focus is to explore the vision, technology, and capabilities of these IoT innovations, recognizing that products not viable today might inspire or become feasible in the future due to advancements in technology and reductions in manufacturing costs. The collection is particularly valuable for a wide array of stakeholders in IoT, including educators, researchers, product designers, and manufacturers.

This data set corresponds to Table II:UNIFORMITY OF TC-PUF DESIGN of manuscript titled "A Lightweight and Secure Physical Unclonable Function Design on FPGA".  The provided data is for FPGA board No. 1 to 15. Board no. 1 to 14  represent uniformity of 40X40 TC-PUF response implemented on Artix-7 FPGA, and board no. 15  represent uniformity of 20X40 TC-PUF response implemented on Zynq Z-7010 FPGA. It is observed that nearly all the TC-PUF implemented on individual FPGAs have a slight bias towards ‘0’.

With the growth of Internet of Things (IoT) applications, the need for accurate indoor positioning systems (IPS) has become urgent. While GPS has limitations in indoor scenarios, Visible Light Positioning (VLP) presents promising results. This paper addresses the challenge of estimating the receiver's height in three-dimensional (3D) positioning scenarios, a crucial problem in VLP. We propose a novel 3D VLP algorithm, adopting a multiple estimation strategy for height estimation to minimize random errors.

This dataset is dedicated to the assesment of cooperative localization algorithms using realistic driving patterns from many vehicles moving in CARLA simulator, along with realistic V2V communication and network quality conditions.  

Smart homes contain programmable electronic devices (mostly IoT) that enable home automation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks.