Optimization in Industrial IoT Networks Based on AI-Technologies: A Review of Various Frameworks and Applications

Abstract

Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) integration is now a pivotal force of digital transformation in contemporary industries. AI enables smart decision-making, predictive control, and automation where IIoT makes scale connectively and real-time data acquisition possible. These technologies combined transform the way industries operate, making them less complex, more efficient, less expensive and with reduced downtime. This review explores the AI-based optimization solutions in IIoT networks with specific focus on benefits of use of machine learning frameworks and their applicability in industries. Some of the methods of optimization including predictive maintenance, anomaly detection, load balancing, and energy efficiency are explained illustrating how they are applicable in industries like manufacturing, logistics, and management of energy use. Open-source machine learning frameworks such as TensorFlow, Porch, and H2O are discussed and their benefits on scalability, flexibility, and efficient deployment of the model pivot around intelligent IIoT solutions. The fact that they have the capacity to support deep learning, reinforcement learning, and real-time analytics highlights their usefulness in undertaking multifaceted industrial work. Communication between variously distributed assets is identified as a key issue and interoperability, cybersecurity risk, and computation limitation are viewed as principal challenges. The paper concludes by providing future implications of knowledge on emerging technologies and future areas of research that can enhance the strength of AI-powered IIoT ecosystems.