Design of a Real-Time Monitoring and Pollution Early Warning System for Industrial Flue Gas Emissions Based on Embedded Edge Computing

Design and application of intelligent monitoring system for road and bridge based on Internet of Things technology

An intelligent monitoring system for roads and bridges based on Internet of Things technology that provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.

An Internet of Things-based intelligent monitoring and fault detection method for the operation and maintenance of offshore wind turbines

The rapid expansion of offshore wind farms has introduced significant challenges to operation and maintenance (O&M), particularly under harsh marine environments where reliable electromagnetic information transmission and constrained wireless communication resources directly affect intelligent monitoring performance. Traditional Supervisory Control and Data Acquisition (SCADA) systems relying on cloud-centric architectures often encounter excessive latency and bandwidth bottlenecks when transmitting high-frequency vibration signals, limiting real-time fault diagnosis. To address these issues, this study proposes an Internet of Things (IoT)-based intelligent monitoring and fault detection method built upon an Edge-Cloud collaborative architecture. A lightweight Adaptive One-Dimensional Convolutional Neural Network (A-1D-CNN) is developed for deployment on edge gateway devices, enabling direct extraction of fault characteristics from raw vibration signals without manual feature engineering. Combined with an “ Edge-Training, Cloud-Update” strategy, the proposed framework continuously optimizes diagnostic performance while substantially reducing communication overhead across wireless sensing and electromagnetic transmission infrastructures. Experimental evaluation on a standard bearing fault dataset demonstrates that the proposed method achieves a fault diagnosis accuracy of 99.25% with a compact model size of only 0.45 MB, providing an effective balance between diagnostic precision and deployment efficiency. The results indicate that the proposed framework offers a practical solution for real-time intelligent monitoring in bandwidth-limited offshore environments and provides technical support for reliable electromagnetic-enabled sensing networks and distributed fault diagnosis in next-generation offshore energy systems.

Optimization of real-time forest monitoring system using yolo v9 object detection and 2.4 ghz wireless network: resource allocation, energy efficiency, and industrial deployment strategies

Large forest areas are increasingly exposed to illegal activities and environmental threats, while conventional monitoring systems suffer from limited coverage, high energy consumption, and delayed response. To address these challenges, this study proposes an optimized real-time forest monitoring system designed for industrial-scale deployment in remote environments. The primary objective is to enhance surveillance efficiency by integrating AI-based object detection, long-range wireless communication, and resource-efficient system design. The proposed system employs ESP32-CAM sensor nodes integrated with 2.4 GHz CPE wireless links and a gateway-based YOLOv9 object detection framework. Bandwidth utilization is optimized through selective transmission of processed detection metadata instead of raw images, while deployment parameters are optimized using simulation-based planning. A web-based monitoring platform with an optimized REST API supports real-time visualization and alert generation. Experimental results show that the system achieves reliable communication up to 500 m with packet loss below 5% and latency under 50 ms at distances up to 300 m. Human detection accuracy reaches 98.5% under optimal conditions, with performance degradation observed in dense vegetation and low-light environments. Energy evaluation confirms sustainable operation, with ESP32 nodes consuming 160 mA and the gateway operating at 3.7 W. Comparative analysis indicates reductions of 37% in deployment cost, 24% in energy consumption, and 51% in latency compared to similar systems. This study concludes that the proposed architecture effectively balances accuracy, scalability, cost, and energy efficiency. The novelty lies in the integrated optimization of edge-based AI detection, selective data transmission, and simulation-driven deployment for industrial forest monitoring.

Reliability of mining safety intelligent monitoring and early warning system based on transformer model and Internet of Things technology

An Embedded System-Based Real-Time Renewable Energy Monitoring Framework

Renewable energy has become a key solution for addressing the increasing global demand for sustainable and environmentally friendly power generation. Effective monitoring of renewable energy systems is essential to ensure efficient operation, minimize energy losses, and improve system reliability. This paper presents an Embedded System-Based Renewable Energy Monitoring System designed for real-time acquisition, processing, and transmission of operational parameters from renewable energy sources such as solar photovoltaic (PV) panels and wind energy systems. The proposed system employs a microcontroller integrated with voltage, current, temperature, and environmental sensors to continuously monitor system performance. The collected data are processed locally and transmitted to a cloud-based monitoring platform through wireless communication technologies such as Wi-Fi or GSM, enabling remote supervision and data visualization. Threshold-based fault detection mechanisms generate alerts whenever abnormal operating conditions are identified, allowing timely maintenance and reducing system downtime. Experimental evaluation demonstrates that the proposed embedded monitoring system provides accurate sensor measurements, low power consumption, reliable wireless communication, and real-time performance monitoring. The system offers a cost-effective, scalable, and energy-efficient solution suitable for residential, industrial, and smart grid renewable energy applications, thereby enhancing operational efficiency and supporting sustainable energy management.

Green Edge Computing Architectures for Real-Time Environmental Monitoring Systems

Environmental monitoring facilitates solutions to major worldwide challenges, including air pollution, climate change, and water resource degradation. Yet, conventional cloud-based IoT systems are unable to provide real-time solutions because of issues like latency, increased energy consumption, and limited scalability. This paper aims to present a positive environmental impact of edge computing for real-time environmental monitoring and provide a sustainable, energy-efficient, low-latency environmental monitoring solution. The Edge Computing Real-Time Environmental Monitoring (ECRM) framework of the paper achieves local data processing and decision-making through the integration of edge intelligence, collective, and low-power machine learning models at edge gateways. The framework achieves system responsiveness and low energy consumption through the integration of energy-aware task scheduling and adaptive data transmission strategies. Processed data for air quality (AQI), CO₂, humidity, and temperature levels substantially reduce the framework's reliance on cloud computing. The framework provides a 39% reduction in energy consumption and a 40% reduction in latency in comparison to established cloud system models. The reductions improve real-time system responsiveness and reduce network traffic. The research demonstrates that combining edge architecture and green computing is a potential solution for sustainable environmental monitoring. The proposed systems align with the goals of computing sustainability and future smart city solutions.