IoT News and Research

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Wireless Sensor Networks: Blockchain and Swarm Intelligence Approach

Researchers introduce BS-SCRM, a novel method combining blockchain and swarm intelligence for secure clustering routing in WSNs, addressing energy efficiency and security challenges. Simulation results demonstrate superior performance in network lifetime, energy consumption, and security compared to existing methods, offering promise for diverse applications from IoT to healthcare.

7 May 2024

RST-Net: Advancing Plant Disease Prediction Using Enlightened Swin Transformer Networks

Researchers introduced RST-Net, a novel deep learning model for plant disease prediction, combining residual convolutional networks and Swin transformers. Testing on a benchmark dataset showed superior performance over state-of-the-art models, with potential applications in smart agriculture and precision farming.

24 Apr 2024

Advancements in Human Action Recognition: A Deep Learning Perspective

The paper explores human action recognition (HAR) methods, emphasizing the transition to deep learning (DL) and computer vision (CV). It discusses the evolution of techniques, including the significance of large datasets and the emergence of HARNet, a DL architecture merging recurrent and convolutional neural networks (CNN).

18 Apr 2024

Edge Computing LoRa Gateway for Real-Time Messaging

Researchers developed an edge-computing LoRaWAN gateway for real-time confirmed messaging, significantly reducing messaging time and network server resource usage. The gateway's innovative design and hardware components optimized performance and lowered costs, making it suitable for diverse IoT applications.

17 Apr 2024

Emergency Communication with UAVs: ISATR Algorithm Optimization

Researchers propose the ISATR algorithm to optimize trajectory planning and resource allocation for UAV-assisted emergency communication. By employing cellular automata for user trajectory prediction and iterative scheduling, the algorithm effectively enhances communication quality in disaster scenarios like earthquakes.

15 Apr 2024

Airborne Particulate Matter Pollution Monitoring in Surface Mines Using IoT and Machine Learning

This study introduced an innovative approach to address airborne particulate matter (PM) pollution in surface mines using Internet of Things (IoT) technology and machine learning (ML) techniques. The study highlighted PM 1.0 as the predominant pollutant and employed a real-time monitoring system to track PM concentrations in ball clay surface mine sites.

12 Apr 2024

Water Quality Prediction Using Explainable AI Models

Researchers propose an AI-driven approach for predicting and managing water quality, crucial for environmental sustainability. Utilizing explainable AI models, they showcase the significance of transparent decision-making in classifying drinkable water, emphasizing the potential of their methodology for real-time monitoring and proactive risk mitigation in water management practices.

10 Apr 2024

Optimizing Thermoelectric Properties with Bayesian Optimization

This study harnesses Bayesian optimization (BO) to enhance the thermoelectric properties of multicomponent III–V materials. Through iterative cycles of thin-film deposition, property measurements, and BO recommendations, the research achieves notable improvements in thermoelectric performance, underscoring the effectiveness of machine learning-driven optimization.

9 Apr 2024

Real-time Monitoring of Lake Water Quality Using a Low-cost IoT System

Researchers from New Zealand introduce a groundbreaking Internet of Things (IoT)-based system for real-time monitoring of lake water quality. This portable and affordable solution utilizes low-cost sensors and IoT technology to provide valuable insights into key water quality parameters, offering a practical tool for environmental monitoring and management.

8 Apr 2024

Smart Libraries: IoT and SDN Revolution

Researchers introduced a cost-effective IoT-based architecture using SDN for smart libraries, enhancing efficiency and reducing operational costs significantly. By integrating RFID and SDN technologies, the system automated processes, improved accuracy, and showcased scalability for modernizing library operations.

1 Apr 2024

MEMS Revolutionizing Wearable Technology

Researchers unveil a groundbreaking approach in wearable technology, integrating MEMS accelerometers with in-sensor computing for real-time gait pattern identification. Through innovative design and optimization, MEMS devices demonstrate robustness and competitive performance, offering significant energy savings potential and paving the way for cost-effective, versatile applications in healthcare and beyond.

21 Mar 2024

NSO-VFC in Internet of Vehicles: Securing the Road Ahead

This article delves into the innovative approach of Network Security Offloading in Vehicle-to-Fog-to-Cloud (NSO-VFC), proposed by researchers from Islamic Azad University, Iran, to address the crucial challenge of securing data offloading in the Internet of Vehicles (IoV) within a fog-cloud federation. By leveraging fog computing and incorporating features such as nonce-based authentication and session key generation, NSO-VFC promises to enhance security, communication performance, and cost-efficiency in IoV environments, paving the way for safer roads, efficient traffic management, and improved environmental monitoring.

14 Mar 2024

MFEM-AI Revolutionizes Physical Education Teaching in Higher Education

Dive into the realm of pedagogical evaluation with the groundbreaking MFEM-AI framework, as showcased in Nature. Leveraging fuzzy logic and the ECSO algorithm, this innovative model offers a comprehensive approach to assessing physical education teaching methods in colleges and universities, enhancing skill performance, learning progress, physical fitness, participation rate, student satisfaction, and overall teaching efficiency.

6 Mar 2024

Hierarchical Federated Learning for Smart City AIoT Systems

Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.

29 Feb 2024

Agricultural Digital Twins for Precision Farming

Researchers demonstrate the transformative potential of agricultural digital twins (DTs) using mandarins as a model crop, showcasing how data-driven decisions at the individual plant level can enhance precision farming, optimize resource allocation, and improve fruit quality, ultimately leading to a paradigm shift in agriculture towards individualized farming practices.

28 Feb 2024

Ensemble Learning for Botnet Detection to Enhance IoT Security

Researchers proposed a novel intrusion detection system (IDS) leveraging ensemble learning and deep neural networks (DNNs) to combat botnet attacks on Internet of Things (IoT) devices. By training device-specific DNN models on heterogeneous IoT data and aggregating predictions through ensemble averaging, the system achieved remarkable accuracy and effectively detected botnet activities. The study's structured methodology, comprehensive evaluation metrics, and ensemble approach offer promise in bolstering IoT security against evolving cyber threats.

21 Feb 2024

Real-time Water Quality Monitoring and Prediction System using IoT and Cloud Computing

Researchers developed a comprehensive system leveraging IoT and cloud computing to monitor and predict drinking water quality in real-time. The system integrates sensors, microcontrollers, web servers, and machine learning models to collect, transmit, analyze, and predict water quality parameters. Machine learning algorithms, particularly decision trees, achieved high accuracy in predicting drinkability, demonstrating the system's potential to enhance water safety and contribute to achieving Sustainable Development Goals.

16 Feb 2024

Dynamic Node Selection for Federated Learning in Space-Air-Ground Information Networks

Researchers addressed challenges in Federated Learning (FL) within Space-Air-Ground Information Networks (SAGIN) by introducing the LCNSFL algorithm. LCNSFL, based on a Double Deep Q Network (DDQN), strategically selects nodes to minimize time and energy costs. Simulation results demonstrate LCNSFL's superiority over traditional methods, offering efficient convergence and resource utilization in dynamic network environments, essential for practical applications in SAGIN.

16 Feb 2024

AIoT's Impact on Sustainable Development: A Bibliometric Analysis

Analyzing 9,182 documents from 1989 to 2022, this study unveils the burgeoning role of Artificial Intelligence of Things (AIoT) in realizing Sustainable Development Goals (SDGs). With a focus on interdisciplinary collaboration, global trends, and thematic evolution, it emphasizes the dynamic synergy between AI, IoT, and sustainability, guiding future endeavors in leveraging technology for global well-being.

12 Feb 2024

A Federated Learning Approach for Passenger Demand Forecasting in Smart Cities

Researchers present a groundbreaking Federated Learning (FL) model for passenger demand forecasting in Smart Cities, focusing on the context of Autonomous Taxis (ATs). The FL approach ensures data privacy by allowing ATs in different regions to collaboratively enhance their demand forecasting models without directly sharing sensitive passenger information. The proposed model outperforms traditional methods, showcasing superior accuracy while addressing privacy concerns in the era of smart and autonomous transportation systems.

1 Feb 2024