Application of Explainable Artificial Intelligence (XAI) on the Evolution of Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) has emerged as a promising paradigm that brings computation, storage, and intelligence closer to the network edge, enabling low-latency and high-bandwidth services. However, as MEC systems become increasingly complex, the need for Explainable Artificial Intelligence (XAI) techniques. The techniques arises to enhance transparency, interpretability, and trustworthiness. Tremendous success in machine learning has led to a novel trend of AI applications for example in transportation, security, medicine, finance, and defense that offer tremendous benefits but cannot explain their decisions and actions to human users. This article explores the application of XAI methods in the evolution of MEC, aiming to look into the evolution and the current trend of XAI to aid academia and researchers in the related field in decision-making processes within MEC systems.