Empowering Real-Time Data Optimizing Framework Using Artificial Intelligence of Things for Sustainable Computing

Abstract

By exploring the future network, smart technologies promote the development of cutting-edge industrial applications. Internet of Things (IoT) systems use sensing approaches to acquire data and control real-time processing and complex tasks. Several techniques have been proposed for coping with environmental behavior in industrial management and reducing the response in crucial circumstances. However, due to the unique and limited constraints of the industrial environment, managing data routing and sustainable development are recent research concerns. In addition, security is essential for industrial communication systems due to the probability of unauthorized access, thus trust level must be improved. The framework addresses real-world challenges in industrial networks by incorporating a lightweight data verification algorithm designed for green communication, reducing energy consumption while maintaining data integrity. First, predictive computing is implemented using ant colony optimization (ACO) based on real-time requirements and selects the dynamic and communication channels for data transmission across the industrial platform. Second, mobile sinks offer more authentic techniques for verifying sensor data and delivering it securely to the cloud servers. The framework was evaluated and validated in a simulation-based environment, revealing a considerable improvement in terms of network throughput, packet drop ratio, connectivity ratio, and network overhead over the existing approaches.