A Survey on Intelligent Computation Offloading and Pricing Strategy in UAV-Enabled MEC Network: Challenges and Research Directions

TL;DR

This survey reviews the use of AI-driven computation offloading and pricing strategies in UAV-enabled MEC networks, highlighting challenges, benefits, and future research directions for optimizing resource allocation and economic models.

Contribution

It provides a comprehensive overview of intelligent offloading and pricing techniques in UAV-MEC networks, identifying key challenges and outlining future research directions.

Findings

AI enhances UAV-MEC network efficiency

Intelligent pricing improves resource utilization

Open research issues identified

Abstract

The Mobile Network Operator (MNO) must select how to delegate Mobile Device (MD) queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.