SECOA: Serial Exponential Coati Optimization Algorithm for MANET routing with link lifetime prediction

IF 5.1 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech Pub Date : 2024-10-22 DOI:10.1016/j.jestch.2024.101869

Neethu Ravindran , R.P. Anto Kumar

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引用次数: 0

Abstract

Mobile Ad-hoc Network (MANET) is a wireless network that operates without a fixed infrastructure and is highly adaptable to changes in speed and connectivity. The source mobile node can transfer the data to any other destination node; however, it has restrictions on energy utilization and lifetime of battery. In order to overcome this, in the literature several optimization-enabled routing algorithms are developed in MANET. In this paper, an algorithm, named Serial Exponential Coati Optimization Algorithm (SECOA) is proposed for MANET routing. Here, the link lifetime (LLT) is predicted using Recurrent Neural Networks (RNN) to ensure reliable and continuous communication. Once LLT prediction is done, nodes with the maximum LLT values are chosen for the routing purpose. To enhance the routing effectiveness, several objective parameters, like energy, distance, trust, and LLT are employed to devise a multi-objective function. Also, it leads to an optimal path using the proposed SECOA approach. In addition, this model is used to extend LLT by choosing best cluster heads of the conventional clusters. Moreover, trust is computed to improve security and enhance cooperation between nodes, which is employed to accelerate the recognition of misbehaving nodes. Finally, the model attained enhanced performance with a maximum energy of 0.895, maximum LLT of 0.758, maximum PDR of 0.889, maximum throughput of 0.895, as well as maximum trust of 0.778.

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SECOA：链路寿命预测城域网路由的序列指数柯蒂优化算法

移动特设局域网（MANET）是一种无线网络，无需固定的基础设施即可运行，对速度和连接的变化具有很强的适应性。源移动节点可以将数据传输到任何其他目标节点，但它在能量利用和电池寿命方面受到限制。为了克服这一问题，文献中提出了几种针对城域网的优化路由算法。本文提出了一种用于城域网路由选择的算法，名为 "串行指数科蒂优化算法（SECOA）"。在此，使用递归神经网络（RNN）预测链路寿命（LLT），以确保通信的可靠性和连续性。一旦完成 LLT 预测，就会选择 LLT 值最大的节点进行路由。为了提高路由效率，我们采用了多个目标参数，如能量、距离、信任度和 LLT，从而设计出一个多目标函数。同时，它还利用所提出的 SECOA 方法得出了一条最优路径。此外，该模型还通过选择传统簇的最佳簇头来扩展 LLT。此外，通过计算信任度来提高安全性并加强节点间的合作，从而加快对行为不端节点的识别。最后，该模型获得了更高的性能，最大能量为 0.895，最大 LLT 为 0.758，最大 PDR 为 0.889，最大吞吐量为 0.895，最大信任度为 0.778。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Science and Technology-An International Journal-Jestech Materials Science-Electronic, Optical and Magnetic Materials

CiteScore

11.20

自引率

3.50%

发文量

153

审稿时长

22 days

期刊介绍： Engineering Science and Technology, an International Journal (JESTECH) (formerly Technology), a peer-reviewed quarterly engineering journal, publishes both theoretical and experimental high quality papers of permanent interest, not previously published in journals, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews and communications in the broadly defined field of engineering science and technology. The scope of JESTECH includes a wide spectrum of subjects including: -Electrical/Electronics and Computer Engineering (Biomedical Engineering and Instrumentation; Coding, Cryptography, and Information Protection; Communications, Networks, Mobile Computing and Distributed Systems; Compilers and Operating Systems; Computer Architecture, Parallel Processing, and Dependability; Computer Vision and Robotics; Control Theory; Electromagnetic Waves, Microwave Techniques and Antennas; Embedded Systems; Integrated Circuits, VLSI Design, Testing, and CAD; Microelectromechanical Systems; Microelectronics, and Electronic Devices and Circuits; Power, Energy and Energy Conversion Systems; Signal, Image, and Speech Processing) -Mechanical and Civil Engineering (Automotive Technologies; Biomechanics; Construction Materials; Design and Manufacturing; Dynamics and Control; Energy Generation, Utilization, Conversion, and Storage; Fluid Mechanics and Hydraulics; Heat and Mass Transfer; Micro-Nano Sciences; Renewable and Sustainable Energy Technologies; Robotics and Mechatronics; Solid Mechanics and Structure; Thermal Sciences) -Metallurgical and Materials Engineering (Advanced Materials Science; Biomaterials; Ceramic and Inorgnanic Materials; Electronic-Magnetic Materials; Energy and Environment; Materials Characterizastion; Metallurgy; Polymers and Nanocomposites)