Energy efficient multipath routing in IoT-wireless sensor network via hybrid optimization and deep learning-based energy prediction.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2025-04-11 DOI:10.1080/0954898X.2025.2476081

G A Senthil, R Prabha, R Renuka Devi

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引用次数: 0

Abstract

Efficient data transmission in Wireless Sensor Networks (WSNs) is a critical challenge. Traditional routing protocols focus on energy efficiency but do not consider other factors that might degrade performance. This research proposes a novel Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm to address these issues, focusing on optimizing energy-efficient data transmission. In the proposed approach, initially, sensor nodes and field dimensions are initialized. Then, K-means clustering is applied to grouping nodes. The Deep Q-Net model is used to predict energy levels of nodes. CH is selected as per the node having higher energy. Multipath routing is performed through the HBWCO algorithm, which optimally selects the best routing paths by considering factors like reliability, residual energy, predicted energy, throughput, and traffic intensity. If link breakage occurs, a route maintenance phase is initiated using Source Link Breakage Warning (SLBW) message strategy to notify the source node about the issue of choosing another path. This work offers a comprehensive approach to enhancing energy efficiency in networks. The suggested HBWCO approach is in contrast to the traditional methods. The HBWCO approach has achieved the highest reliability of 0.948 and the highest throughput of 3496. Therefore, the HBWCO algorithm offers an effective solution for data transmission and routing reliability.

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基于混合优化和基于深度学习的能量预测的物联网无线传感器网络中的节能多路径路由。

有效的数据传输是无线传感器网络（WSNs）面临的一个关键挑战。传统的路由协议注重能源效率，而不考虑其他可能降低性能的因素。本研究提出了一种新的白鲸-浣熊混合优化（HBWCO）算法来解决这些问题，重点是优化节能数据传输。在该方法中，首先初始化传感器节点和场维。然后，采用K-means聚类对节点进行分组。Deep Q-Net模型用于预测节点的能级。根据能量较高的节点选择CH。采用HBWCO算法进行多路径路由，该算法综合考虑可靠性、剩余能量、预测能量、吞吐量、流量强度等因素，优选出最优的路由路径。如果发生链路中断，则使用源链路中断警告（SLBW）消息策略启动路由维护阶段，通知源节点选择另一条路径的问题。这项工作为提高网络的能源效率提供了一种全面的方法。建议的HBWCO方法与传统方法形成对比。HBWCO方法获得了0.948的最高信度和3496的最高吞吐量。因此，HBWCO算法为数据传输和路由可靠性提供了有效的解决方案。

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

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.