How intelligence is reshaping today: IoE edge networks?

The rapid advancement of Internet of Everything (IoE) devices, coupled with emergent communication technologies such as Sixth Generation (6G), is facilitating the development of IoE-based edge networks wherein services are rendered closer to the network periphery. In these networks, devices exchange information and share computational resources. This research paper presents a novel methodological framework designed to optimise task allocation and device selection in IoE-6G systems. By integrating intelligent algorithms, our study investigates the impact of device selection delay, computational efficiency, and task success rates on overall system performance. The unique capabilities of 6G, such as AI-native infrastructure, Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), Terahertz (THz) frequency bands, and dynamic network slicing, form the foundational enablers of our proposed framework. These features are essential for scalable and real-time IoE operations and are tightly integrated into our proposed system’s algorithmic design. Our methodological framework involves extensive simulations that evaluate the proposed system across various scenarios, focusing on foundational concepts and performance metrics that are essential for understanding the parameters influencing our research outcomes. We provide a detailed comparison of traditional and intelligent scheduling algorithms, showcasing significant improvements in task allocation and completion times when intelligence is employed. The novelty of Intelligent Main Task Off-loading Scheduling Algorithm ( i MTOSA) lies in its dynamic intelligence and adaptive scheduling, optimising IoE task offloading with minimal communication and enhanced scalability by focusing on advanced groups of metrics and thoroughly discussing the implications for real-world IoE-6G environments. Our results contribute to a deeper understanding of the integration of intelligent systems in modern communication networks, paving the way for future advancements in IoE technologies. In overall performance, the intelligent variants of the proposed algorithm show less than 50% affected tasks, while non-intelligent scheduling algorithms exceed 90% affected tasks.