NCRDAP: An AI-driven clustering routing and data aggregation protocol for energy-efficient wireless sensor networks

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech Pub Date : 2025-09-16 DOI:10.1016/j.jestch.2025.102184

MD Jiabul Hoque , Md. Saiful Islam , Istiaque Ahmed

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引用次数: 0

Abstract

Wireless Sensor Networks (WSNs) play a pivotal role in numerous Internet of Things (IoT) applications; however, their performance remains constrained by limited energy resources, inefficient clustering, suboptimal routing, and redundant data transmissions. To address these persistent challenges, this study hypothesizes that integrating intelligent optimization techniques can simultaneously improve energy efficiency, network longevity, and data reliability in WSNs. Accordingly, we propose a novel AI-driven framework titled Neural-optimized Clustering, Routing, and Data Aggregation Protocol (NCRDAP). The framework combines an Artificial Neural Network (ANN)-based Cluster Head (CH) selection mechanism, an enhanced Quantum Particle Swarm Optimization (QPSO) for multi-hop routing, and a dual-step data aggregation strategy using edge computing to reduce redundancy and minimize communication overhead. The methodology was implemented and evaluated through extensive simulations using MATLAB R2022a, incorporating widely accepted radio energy models and comparative benchmarks including Low Energy Adaptive Clustering Hierarchy (LEACH), LEACH-Centralized (LEACH-C), LEACH with Genetic Algorithm (LEACH-GA), Cluster-Based Data Aggregation (CBDA), Power-efficient and Scalable Adaptive Network (PSAN), and Energy-aware Network Selection (ENS) protocols. The experimental results demonstrate that NCRDAP extends network lifetime by 19–23 %, enhances throughput by 20–30 %, and reduces overall energy consumption and packet loss ratio compared to existing techniques. Furthermore, QPSO exhibited faster convergence behavior and superior routing efficiency, while the dual-step edge processing strategy significantly reduced redundant transmissions without imposing substantial computational overhead. These findings confirm that the proposed NCRDAP framework offers a scalable, energy-efficient, and reliable solution for real-time, resource-constrained WSN applications.

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NCRDAP：一种用于高能效无线传感器网络的ai驱动的集群路由和数据聚合协议

无线传感器网络（wsn）在众多物联网（IoT）应用中发挥着关键作用；然而，它们的性能仍然受到有限的能源、低效的集群、次优路由和冗余数据传输的限制。为了解决这些持续存在的挑战，本研究假设集成智能优化技术可以同时提高wsn的能源效率、网络寿命和数据可靠性。因此，我们提出了一种新的人工智能驱动框架，名为神经优化聚类、路由和数据聚合协议（NCRDAP）。该框架结合了基于人工神经网络（ANN）的簇头（CH）选择机制、用于多跳路由的增强型量子粒子群优化（QPSO）以及使用边缘计算的双步数据聚合策略来减少冗余和最小化通信开销。该方法通过使用MATLAB R2022a进行大量模拟来实现和评估，并结合了广泛接受的无线电能量模型和比较基准，包括低能量自适应聚类层次（LEACH）， LEACH-中心化（LEACH- c）， LEACH遗传算法（LEACH- ga），基于簇的数据聚合（CBDA），节能和可扩展自适应网络（PSAN）以及能量感知网络选择（ENS）协议。实验结果表明，与现有技术相比，NCRDAP延长了19 - 23%的网络寿命，提高了20 - 30%的吞吐量，降低了总体能耗和丢包率。此外，QPSO具有更快的收敛行为和优越的路由效率，而双步边缘处理策略在不增加大量计算开销的情况下显著减少了冗余传输。这些发现证实了所提出的NCRDAP框架为实时、资源受限的WSN应用提供了可扩展、节能和可靠的解决方案。

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

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)