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An Intelligent Framework for Resilience Recovery of FANETs with spatio-temporal Aggregation and Multi-Head Attention Mechanism

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Computers, Materials & Continua 2024年第5期79卷 2375-2398页

作者： Zhijun Guo Yun Sun YingWang Chaoqi Fu Jilong Zhong Air Traffic Control and Navigation College Air Force Engineering UniversityXi’an710000China Institute of Systems Engineering Academy of Military ScienceBeijing100101China Equipment Management and UAV Engineering College Air Force Engineering UniversityXi’an710038China Defense Innovation Institute Academy of Military ScienceBeijing100071China

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

关键词： Resilience cooperative mission FANET spatio-temporal node pooling multi-head attention graph network

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