Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model

To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Extensive analysis and numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.

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Publication Details

Journal
IEEE Transactions on Network Science and Engineering
Published
2024-10-17
DOI
https://doi.org/10.1109/tnse.2024.3482327
Citations
19
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
6.63

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article

Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model

Xiaohuan Li, Xin Tang, Rong Yu, Qian Chen
19 citations
IEEE Transactions on Network Science and Engineering
Digital Transformation in Industry
6.63
article

Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model

Xiaohuan Li, Xin Tang, Rong Yu, Qian Chen
article en
19 citations

Abstract

To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Extensive analysis and numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.

IEEE Transactions on Network Science and EngineeringVol. 12(1)
Guangdong University of Technology (CN), Guangxi University (CN), Guilin University of Electronic Technology (CN)
National Natural Science Foundation of China
Partnerships for the goals
Openalex Percentile: Top 3%
Digital Transformation in Industry
6.63
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Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model — Xiaohuan Li, Xin Tang, et al. · IEEE Transactions on Network Science and Engineering (2024) | TGRS Research Map | TGRS