Open Access

ARTICLE

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

Yuting Zhu¹, Yanfang Fu^2,*, Yang Ce³, Pan Deng¹, Jianpeng Zhu¹, Huankun Su¹

1 China Electronics Technology Group Corporation 20th Research Institute, Xi’an, 710068, China
2 School of Computer Science and Engineering, Xi’an Technological University, Xi’an, 710021, China
3 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada

* Corresponding Author: Yanfang Fu. Email:

Computers, Materials & Continua 2023, 77(3), 3867-3894. https://doi.org/10.32604/cmc.2023.046772

Received 14 October 2023; Accepted 21 November 2023; Issue published 26 December 2023

Abstract

In light of the escalating demand and intricacy of services in contemporary terrestrial, maritime, and aerial combat operations, there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks. Software-Defined Networking (SDN) proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts, due to its intrinsic ability to flexibly allocate and centrally administer network resources. This study pivots around the optimization of SDN controller deployment within airborne data link clusters. A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed. Within this architectural framework, the controller deployment issue is reframed as a two-fold problem: subdomain partitioning and central interaction node selection. We advocate a subdomain segmentation approach grounded in node value ranking (NDVR) and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm (AFSA). The advanced NDVR-AFSA (Node value ranking-Improved artificial fish swarm algorithm) algorithm makes use of a chaos algorithm for population initialization, boosting population diversity and circumventing premature algorithm convergence. By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations, the algorithm’s search range adaptability is enhanced, thereby increasing the possibility of obtaining globally optimal solutions, while concurrently augmenting cluster reliability. The simulation results verify the advantages of the NDVR-IAFSA algorithm, achieve a better load balancing effect, improve the reliability of aviation data link cluster, and significantly reduce the average propagation delay and disconnection rate, respectively, by 12.8% and 11.7%. This shows that the optimization scheme has important significance in practical application, and can meet the high requirements of modern sea, land, and air operations to aviation airborne communication networks.

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