OPTIMIZING DATA TRANSMISSION IN CLUSTERED MULTI-EDGE COMPUTING FOR INTELLIGENT IOT

Abstract

This paper focuses on the challenges of optimizing data transmission in clustered Multi-Access Edge Computing (MEC) systems for Internet of Things (IoT) applications. With all of this proliferation of IoT devices, the traditional cloud-based architectures are limited by means of latency, bandwidth and energy efficiency. To address these challenges, this work introduces a novel data transmission optimization model which leverages dynamic clustering, reinforcement learning based task offloading, and adaptive routing approaches to optimize system performance. The proposed model aims to minimize end-to-end latency, energy consumption, and maximize throughput and packet delivery ratio (PDR) in a large-scale IoT environment. To assess the effectiveness of the proposed model the simulations were carried out with respect to static clustering and threshold offloading baseline models. The results validate the superiority of the proposed system with respect to the key performance metrics compared to the baseline systems. In particular, the proposed model achieved up to 40% less latency, 31-35% better energy efficiency, as well as a higher PDR and throughput than static clustering and threshold offloading. Furthermore, the proposed system exhibited cluster stability for 120 minutes which is much larger than that of baseline models (75-90 minutes). Moreover, the sensitivity analysis indicated that the proposed model is scalable and adaptable and works well in different node density and traffic loads. The results demonstrate the promise of MEC for making large-scale IoT networks energy efficient, low latency, and efficient. The findings of this research could help to optimize the data transmission in MEC-based IoT systems, which have potential applications in smart cities, healthcare, and industrial automation fields.