Intelligent resource management in 5G/6G network by adopting edge intelligence for higher education systems

• Published in: e-Prime 2024-06, Vol.8, p.100517, Article 100517
• Main Authors: Chandra, Saurabh, Arya, Rajeev, Singh, Maheshwari Prasad
• Format: Article
• Language: English
• Subjects: Device-to-Device Communication; Energy Consumption; Intelligent Resource Management; Latency; Power consumption; Social Internet of Educational Things
• ISSN: 2772-6711; 2772-6711
• DOI: 10.1016/j.prime.2024.100517

•A novel Social Internet of Educational Things (IoT) framework with D2D-assisted edge intelligence network system.•In an Intelligence Edge-driven D2D communication, jointly optimizes the latency with intelligent resource allocation and power consumption which aims to minimize the energy consumption from each device.•A D2D-assisted intelligent resource allocation approach is proposed by using the concept of lagrangian and bisection method.•The proposed approach has been validated through different performance parameters and comparisons with benchmark schemes. The Internet of Things (IoT) is expected to have a positive impact on various sectors, such as manufacturing, users, and operators. Due to the capabilities of smart objects to share their contents and interact with their social networks, the combined Social Internet of Educational Things (SIoET) for smart education is expected to provide users with more efficient services. The primary objective of this paper is to provide an efficient resource management by adopting edge intelligence technique for higher education system workloads on next-generation networks. As the number of tasks handled by these interconnected devices continues to rise, there is an escalating concern regarding resource competition and power consumption within the network. To mitigate these challenges, we propose an innovative edge intelligence-based resource allocation approach to intelligently allocate resources while minimizing latency and optimizing power consumption with increasing task volumes. The problem is dissected into two core sub-problems: computation resource optimization and power optimization. Leveraging the concept of Lagrangian and Bisection methods, our proposed algorithm, implemented within a Device-to-Device (D2D) -assisted edge intelligence network, demonstrates remarkable performance improvements. Network latency and energy consumption is reduced by up to 57.9 % and 82.3 % compared to the benchmark schemes. These findings hold promising implications for the integration of this work into higher education systems driven by SIoET. Furthermore, simulations indicate that the system maintains robust performance even in dynamic urban environments characterized by random device movement.