Open Access

ARTICLE

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

Zhijun Guo¹, Yun Sun^2,*, Ying Wang¹, Chaoqi Fu³, Jilong Zhong^4,*

1 Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an, 710000, China
2 Institute of Systems Engineering, Academy of Military Science, Beijing, 100101, China
3 Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, 710038, China
4 Defense Innovation Institute, Academy of Military Science, Beijing, 100071, China

* Corresponding Authors: Yun Sun. Email: ; Jilong Zhong. Email:

(This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)

Computers, Materials & Continua 2024, 79(2), 2375-2398. https://doi.org/10.32604/cmc.2024.048112

Received 28 November 2023; Accepted 22 March 2024; Issue published 15 May 2024

Abstract

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 coverage mission 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.

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Keywords

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