Oussama Lagnfdi, Marouane Myyara, and Anouar Darif

A New Deep Learning-Based Approach for IoT Task Offloading in Multi-access Edge Computing 

The exponential growth of Internet of Things (IoT) devices and the growing demand for resource-intensive applications have introduced significant challenges in computation, storage, and network efficiency. Although cloud computing provides partial relief, its centralized nature leads to unacceptable latency for delay-sensitive applications. Multi-access Edge Computing (MEC), especially with the advent of 5G, has emerged as a compelling solution by relocating computation closer to data sources, thereby reducing latency and improving responsiveness in applications such as smart agriculture, autonomous vehicles, augmented reality, and telemedicine. However, efficient workload offloading in MEC environments remains complex due to system heterogeneity, varying application requirements, and limited edge resources. This paper proposes a novel neural network-based approach to computation offloading in MEC, integrating workload allocation and resource management while accounting for application delay sensitivity, processing capacity, and communication constraints. The proposed model enables driving offloading decisions, adapting to fluctuating system states without relying on complex mathematical formulations. Simulation results demonstrate that the approach significantly reduces service time and enhances resource utilization, ensuring responsiveness for modern IoT applications. This research underscores MEC’s potential to meet the rising computational and latency demands of next-generation IoT infrastructure.

Reference:

DOI: 10.36244/ICJ.2026.1.2

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Please cite this paper the following way:

Oussama Lagnfdi, Marouane Myyara, and Anouar Darif, "A New Deep Learning-Based Approach for IoT Task Offloading in Multi-access Edge Computing  ", Infocommunications Journal, Vol. XVIII, No 1, March 2026, pp. 11-18., https://doi.org/10.36244/ICJ.2026.1.2