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Optimal routing strategy based on extreme learning machine with beetle antennae search algorithm for Low Earth Orbit satellite communication networks
International Journal of Satellite Communications and Networking ( IF 1.7 ) Pub Date : 2020-12-22 , DOI: 10.1002/sat.1391
Aghila Rajagopal 1 , A. Ramachandran 2 , K. Shankar 3 , Manju Khari 4 , Sudan Jha 5 , Gyanendra Prasad Joshi 6
Affiliation  

Due to the significant utilization of terrestrial communication, Low Earth Orbit (LEO) satellite network is a critical part of satellite communication networks owing to its several benefits. But the efficient and trustworthy routing for LEO satellite networks (LSNs) is a difficult process because of dynamic topology, adequate link changes, and imbalanced communication load. This study devises a new hybridization of extreme learning machine (ELM) with multitask beetle antennae search (MBAS) algorithm‐based distributed routing called the MBAS‐ELM model. The proposed model determines the routes based on traffic forecasting with respect to the level of traffic circulation on the earth. The proposed method is employed for traffic forecasting at the satellite nodes (SNs). To identify the optimal routes, mobile agents (MAs) are applied to concurrently and autonomously determine for LSNs and make a decision on routing data. The experimental outcome has showcased the effective performance of the proposed model over the compared models in terms of different measures, namely, average delay, packet loss ratio (PLR), and queuing delay. The results are validated under varying simulation time and data sensing rates. The obtained outcome pointed out the superior performance of the proposed MBAS‐ELM model compared with other methods.

中文翻译:

基于极限学习机和甲虫天线搜索算法的低地球轨道卫星通信网络最优路由策略

由于对地面通信的大量利用,低地球轨道(LEO)卫星网络由于具有多种优势而成为卫星通信网络的重要组成部分。但是,由于动态拓扑,适当的链路更改和不平衡的通信负载,LEO卫星网络(LSN)的有效且可信赖的路由是一个困难的过程。这项研究设计了一种新的极限学习机(ELM)与基于多任务甲虫天线搜索(MBAS)算法的分布式路由混合技术,称为MBAS-ELM模型。所提出的模型基于与地球上的交通流水平有关的交通预测来确定路线。所提出的方法被用于在卫星节点(SN)处的业务量预测。为了确定最佳路线,移动代理(MA)用于同时并自主地确定LSN,并就路由数据做出决策。实验结果表明,相比于比较模型,该模型在不同度量(即平均延迟,丢包率(PLR)和排队延迟)方面具有有效的性能。结果在不同的仿真时间和数据感测速率下得到了验证。获得的结果表明,与其他方法相比,所提出的MBAS-ELM模型具有优越的性能。结果在不同的仿真时间和数据感测速率下得到了验证。获得的结果表明,与其他方法相比,所提出的MBAS-ELM模型具有优越的性能。结果在不同的仿真时间和数据感测速率下得到了验证。获得的结果表明,与其他方法相比,所提出的MBAS-ELM模型具有优越的性能。
更新日期:2020-12-22
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