IoT News and Research

RSS

Affordable Wireless Energy Meter for Accurate Power Quality Monitoring in Smart Grids

Researchers introduce a cost-effective wireless energy meter employing the ESP32 microcontroller for power quality monitoring in smart grid applications. By integrating sentiment analysis using Word2vec and LSTM, the model efficiently captures emotional influences on the global economy, leading to improved accuracy and reduced energy consumption.

19 Aug 2023

TE-VS: Wearable Marvel for Motion Monitoring and Energy Harvesting

A groundbreaking innovation, the TE-VS combines triboelectrification and electromagnetic power generation to revolutionize wearables. With machine learning integration and applications in healthcare and sustainable energy, the TE-VS promises accurate motion monitoring and energy harvesting, shaping a brighter future for technology and well-being.

17 Aug 2023

Empowering Cities: Citizen-Centric Digital Twins for Enhanced Governance

Researchers delve into the realm of Citizen-Centric Digital Twins (CCDT), exploring cutting-edge technologies that enable predictive, simulative, and visualizing capabilities to address city-level issues through citizen engagement. The study highlights data acquisition methods, machine learning algorithms, and APIs, offering insights into enhancing urban management while fostering public participation.

17 Aug 2023

Revolutionizing Plant Seedling Monitoring with GL-CNN and UAV Technology

Researchers introduce the Gap Layer modified Convolution Neural Network (GL-CNN) coupled with IoT and Unmanned Aerial Vehicles (UAVs) for accurate and efficient monitoring of palm tree seedling growth. This approach utilizes advanced image analysis techniques to predict seedling health, addressing challenges in early-stage plant monitoring and restoration efforts. The GL-CNN architecture achieves impressive accuracy, highlighting its potential for transforming ecological monitoring in smart farming.

15 Aug 2023

AI and Big Data Revolutionizing Low-Carbon Buildings: Challenges and Promises

Researchers delve into AI's role in carbon reduction in buildings, discussing energy prediction, ML-driven emission mitigation, and carbon accounting. The paper underscores urgent emission reduction in construction, highlighting ML's potential to drive sustainable practices, with a focus on AI's positive impact on the low-carbon building sector.

15 Aug 2023

Decoding Emotions: Neural Networks Revolutionize Facial Emotion Recognition

The paper delves into recent advancements in facial emotion recognition (FER) through neural networks, highlighting the prominence of convolutional neural networks (CNNs), and addressing challenges like authenticity and diversity in datasets, with a focus on integrating emotional intelligence into AI systems for improved human interaction.

13 Aug 2023

AI-Powered Threat Hunting for Critical Infrastructure Protection

Researchers present a distributed, scalable machine learning-based threat-hunting system tailored to the unique demands of critical infrastructure. By harnessing artificial intelligence and machine learning techniques, this system empowers cyber-security experts to analyze vast amounts of data in real-time, distinguishing between benign and malicious activities, and paving the way for enhanced threat detection and protection.

11 Aug 2023

Deep Learning Safeguards: Strengthening Cybersecurity in Industry 5.0

This article introduces cutting-edge deep learning techniques as a solution to combat evolving web-based attacks in the context of Industry 5.0. By merging human expertise and advanced models, the study proposes a comprehensive approach to fortify cybersecurity, ensuring a safer and more resilient future for transformative technologies.

10 Aug 2023

Boosting Energy Efficiency in 6G Networks with Intelligent Reflecting Surfaces

Researchers delve into the transformative potential of Intelligent Reflecting Surfaces (IRSs) within 6G wireless networks, presenting a model that enhances communication efficiency and energy conservation. A case study involving smart ocean transportation illustrates the advantages of using IRSs, offering real-world insights into their practical implementation for Industry 5.0 applications.

10 Aug 2023

Leveraging Transfer Learning for Intelligent 6G Networks

Researchers discuss the integration of artificial intelligence (AI) and networking in 6G networks to achieve efficient connectivity and distributed intelligence. It explores the use of Transfer Learning (TL) algorithms in 6G wireless networks, demonstrating their potential in optimizing learning processes for resource-constrained IoT devices and various IoT paradigms such as Vehicular IoT, Satellite IoT, and Industrial IoT. The study emphasizes the importance of optimizing TL factors like layer selection and training data size for effective TL solutions in 6G technology's distributed intelligence networks.

5 Aug 2023

Enhancing Smart Cities with Dual-Branch Residual Networks for Urban Sound Classification

Researchers from China Jiliang University and Hangzhou Aihua Intelligent Technology Co., Ltd. propose a novel approach using dual-branch residual networks to enhance urban environmental sound classification in smart cities. By accurately identifying and classifying various sounds, this advanced system offers valuable insights for city management, security, environmental monitoring, traffic management, and urban planning, leading to more livable and sustainable urban environments.

2 Aug 2023

TwinPort Integrates Digital Twin and Drones for Precise Ship Maneuvering

Researchers propose TwinPort, a cutting-edge architecture that combines digital twin technology and drone-assisted data collection to achieve precise ship maneuvering in congested ports. The approach incorporates a recommendation engine to optimize navigation during the docking process, leading to enhanced efficiency, reduced fuel consumption, and minimized environmental impact in smart seaports.

1 Aug 2023

Aeras Intel Launches as Independent Entity, Revolutionizing Healthcare with IoT Innovations

Aeras Intel, a visionary MedTech company, is proud to announce its official launch as an independent company. Aeras Intel's origins come from IoT innovations within the dental manufacturer DENTALEZ and its unyielding mission to simplify all aspects of managing critical equipment and redirecting the time and energy savings to improving the user and patient experiences. Aeras Intel is now operating as a privately held, independent Delaware-based corporation, Aeras, Inc.

27 Jul 2023

Precision Agriculture: Automated Grading and Sorting of Carrots Using Computer Vision

A groundbreaking study presents a framework that leverages computer vision and artificial intelligence to automate the inspection process in the food industry, specifically for grading and sorting carrots. By incorporating RGB and depth information from a depth sensor, the system accurately identifies the geometric properties of carrots in real-time, revolutionizing traditional grading methods.

12 Jul 2023

Revolutionizing Artificial Neural Networks: Self-Powered In-Sensor ANNs with Molecular Ferroelectric Photomemristors

Researchers have achieved a breakthrough in the development of artificial neural networks (ANNs) by implementing self-powered in-sensor ANNs using molecular ferroelectric (MF)-based photomemristor arrays. These innovative devices enable real-time optical signal recognition and acquisition while minimizing power consumption.

12 Jul 2023

Smart Seas: Revolutionizing Aquaculture with AIoT and Digital Twin-Based Intelligent Fish Farming

The integration of AIoT and digital twin technology in aquaculture holds the key to revolutionizing fish farming. By combining real-time data collection, cloud computing, and AI functionalities, intelligent fish farming systems enable remote monitoring, precise fish health assessment, optimized feeding strategies, and enhanced productivity. This integration presents significant implications for the industry, paving the way for sustainable practices and improved food security.

11 Jul 2023

Sky High Insights: Estimating Building Heights with DLIPHE Algorithm and Google Street View

Researchers propose DLIPHE, a novel algorithm that combines deep learning and image processing, to estimate building heights using static Google Street View images. The algorithm employs semantic segmentation and advanced techniques to identify buildings and extract their contours, enabling real-time and automatic height estimation for aerial devices. The study demonstrates promising results, highlighting the potential for DLIPHE to enhance communication paths for unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOLs) in future urban networks.

10 Jul 2023

Unlocking Secure Homes: Blockchain and Machine Learning Revolutionize Smart Home Networks

Researchers propose a groundbreaking solution in the form of a blockchain layer and an enhanced Dragonfly algorithm to fortify smart home networks. This innovative approach ensures reliable user authentication, safeguards data privacy, and optimizes communication while paving the way for further advancements such as 5G integration and edge computing, promising secure and efficient smart homes of the future.

9 Jul 2023

Unleashing the Power of Text Mining: Identifying AI Technologies for the Korean Semiconductor Industry

The study demonstrates the use of text mining to identify emerging ML/AI technologies in the Korean semiconductor industry, enabling SMEs to establish an R&D roadmap and enhance competitiveness. Deep neural networks and AI technology applications in semiconductor R&D and manufacturing processes were found to be crucial, with potential for improved reasoning, learning abilities, and process optimization.

6 Jul 2023

Safeguarding IoT: Intelligent Anomaly Detection with Federated Learning and Machine Learning

The study proposes a smart system for monitoring and detecting anomalies in IoT devices by leveraging federated learning and machine learning techniques. The system analyzes system call traces to detect intrusions, achieving high accuracy in classifying benign and malicious samples while ensuring data privacy. Future research directions include incorporating deep learning techniques, implementing multi-class classification, and adapting the system to handle the scale and complexity of IoT deployments.