2024· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
An Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring that improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing.
Abstract
The rapid evolution of Industry 4.0 has accelerated the adoption of intelligent manufacturing systems requiring real-time monitoring, predictive maintenance, and autonomous decision-making. Traditional cloud-based solutions often suffer from latency, bandwidth limitations, and data privacy concerns, making them unsuitable for time-critical industrial applications. This paper presents an Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things (IIoT) sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring. Real-time sensor data, including temperature, vibration, pressure, humidity, and power consumption, are processed locally using Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and anomaly detection algorithms to identify equipment faults and optimize operations. Only summarized insights are transmitted to the cloud, reducing communication overhead while enabling scalable enterprise-level analytics. The proposed framework improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing. It provides a scalable and resilient solution for smart factories across industries, enabling intelligent automation, predictive analytics, and efficient autonomous industrial operations.
The rapid advancement of Industry 4.0 has accelerated the development of intelligent and autonomous smart factories powered by Industrial Internet of Things (IIoT) devices, cyber-physical systems (CPS), and advanced manufacturing technologies. Although cloud computing offers significant computational and storage capabilities, it suffers from latency, bandwidth limitations, privacy concerns, and delayed decision-making in time-critical industrial environments. This paper proposes an Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration. The framework employs machine learning for anomaly detection, deep learning for automated quality inspection, reinforcement learning for adaptive production scheduling, and predictive models for equipment health monitoring. Secure communication protocols enhance data protection and system reliability. Experimental results demonstrate improved latency, prediction accuracy, manufacturing efficiency, energy utilization, fault detection, and operational resilience compared with conventional cloud-based approaches. The proposed architecture provides a scalable and sustainable solution for next-generation autonomous smart factories, improving equipment reliability, reducing operational costs, and increasing manufacturing productivity.
Seshagiri N· International Journal of Mod...· 0 citations
The Fourth Industrial Revolution is accelerating the adoption of Industry 4.0 through intelligent computing, Industrial Internet of Things (IIoT), edge computing, and AI-driven automation. Traditional cloud-based industrial systems often experience latency, bandwidth limitations, network congestion, and privacy concerns, making them unsuitable for real-time manufacturing applications. This paper proposes an Edge Intelligence framework that integrates distributed edge computing, real-time AI analytics, and autonomous decision-making to process industrial IoT data locally. The architecture consists of four layers: perception, edge intelligence, autonomous decision, and cloud coordination. Industrial sensors, PLCs, and robotic systems collect operational data, while lightweight machine learning, deep learning, and reinforcement learning models perform feature extraction, anomaly detection, predictive analytics, and adaptive control with minimal latency. A mathematical optimization model minimizes processing delay, energy consumption, and resource utilization while maximizing accuracy and efficiency. Experimental evaluation demonstrates improved real-time performance, decision accuracy, fault detection, scalability, and resource utilization, making the framework well suited for next-generation smart manufacturing and sustainable industrial automation.
V. Sethi· International Journal of Int...· 0 citations
Analytical preservation has become an vital strategy in Industrial Internet of Things (IIoT) locations for enlightening equipment reliability, dropping unforeseen machine failures, and enlightening trade productivity. This paper suggests an AI-driven predictive maintenance framework using the Google Cloud AI Platform for smart monitoring and fault estimate of engineering refining machines. The planned framework assembles and procedures real-time device data such as vibration, temperature, turning speed, and instrument wear from IIoT-enabled plans. Three machine learning algorithms, namely Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are practical and assessed to classify potential equipment letdowns before disappointment occurrence. Among the manufacturing models, the XGBoost classifier achieved the highest prediction accuracy of 99.18% with strong accuracy, recall, and AUC performance, on behalf of superior ability in early fault finding and analytical analytics. The grouping of Google Cloud AI services allows walkable model training, cloud-based supply, real-time specialist care, and efficient data organization for smart manufacturing applications. New results show significant improvements in upkeep efficiency, decrease in working downtime, and lower upkeep costs associated with traditional sensitive conservation approaches. The study highlights the productivity of joining IIoT sensor analytics, cloud computation, and progressive artificial intelligence methods for evolving smart and proactive industrial conservation arrangements in Industry 4.0 surroundings.
More Praveen, A. Lakshman, V.Jyothi2 et al.· 2026 4th International Confe...· 0 citations
Key performance indicators, including production efficiency, resource utilization, product quality, energy efficiency, downtime reduction, and system reliability, demonstrate the effectiveness of the proposed Digital Twins framework.
Suresh Babu Reddy· International Journal of App...· 0 citations
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
The CPS, in combination with the IoT sensor networks, has experienced massive growth, which results in massive data generation per second that presents extreme challenges to latency, scalability, and efficient data processing. The current paper presents a cloud-edge-integrated machine learning system for real-time monitoring of CPS environments. The suggested system combines IoT data collection, edge processing, and cloud-based model optimisation to enable fast, intelligent decision-making. Edge computing reduces communication overhead by performing local inference, while the cloud provides large-scale analytics and model training. The evaluation of the framework is conducted on a dataset of 10,000 sensor records that represent industrial parameters such as temperature, pressure, and vibration. The experimental findings showed that prediction accuracy was 91.2%, processing efficiency was 88.5%, and stability was 0.86, with a much lower latency of 205 ms. The overall performance index of 0.88 indicates that the computer's responsiveness, scalability, and efficiency have improved equally. The comparative analysis demonstrates that the proposed approach is significantly superior to traditional and standalone machine learning models, which is why it can be widely applied in real-time CPS monitoring applications.
Jayan Sharma· International Journal on Eng...· 0 citations
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