高效多级自适应聚类提高水下传感器网络性能

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Emad S. Hassan
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引用次数: 0

摘要

水下无线传感器网络(UWSNs)对于水下环境的实时数据采集和监测至关重要,在环境保护、灾害管理、海洋生物多样性监测和海上能源生产等领域有着广泛的应用。然而,UWSN的部署面临着巨大的挑战,如高能耗、有限的通信范围、信号衰减以及水下水流和温度变化等环境因素。现有的解决方案在更深、更复杂的水下环境中缺乏可扩展性和适应性。本文提出了一种高效节能的多级自适应聚类(EEMLAC)方案,旨在优化uwsn的能耗,提高其网络寿命。该方法基于深度和水压动态调整聚类结构,优化信号管理,最小化能量损失。节点被分为三个自适应层次:第一级,在较浅的深度,将外部和中间部分以120°角划分为三个分段;在中深度的第二层,以90°角将划分为四个分段,以减轻更高的信号衰减;第三层,在最深处,进一步细化聚类,以60°角的六个子部分,以有效地管理数据流和能源使用。普通节点位于中段,直接与簇头通信,高级节点位于外侧,利用辅助节点中继数据,减少传输能耗。该方案通过适应水压和衰减等环境因素,有效解决了深水水下无线传感器网络的通信挑战。仿真结果表明,EEMLAC在1000轮后保留了约0.6 J的剩余能量,将能量消耗降低到2.3 × 10−3 J /节点,将网络寿命延长到1800轮以上,实现了0.586的分组传输比(PDR),并将吞吐量提高到3.7 × 106 bits/s,优于EBREC, EAMC和EGRCs等基准方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy-Efficient Multi-Level Adaptive Clustering for Enhanced Performance in Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) are vital for real-time data collection and monitoring in underwater environments, with applications in environmental conservation, disaster management, marine biodiversity monitoring, and offshore energy production. However, UWSN deployment faces significant challenges such as high energy consumption, limited communication range, signal attenuation, and environmental factors like underwater currents and temperature variations. Existing solutions suffer from scalability and adaptability in deeper and more complex underwater environments. This paper presents an Energy-Efficient Multi-Level Adaptive Clustering (EEMLAC) scheme designed to optimize energy consumption and enhance the network lifetime of UWSNs. The proposed approach dynamically adjusts clustering structures based on depth and water pressure, optimizing signal management and minimizing energy loss. Nodes are categorized into three adaptive levels: the first level, at shallower depths, divides the outer and middle sections into three subsections at 120° angles; the second level, at mid-depths, increases the division to four subsections at 90° angles to mitigate higher signal attenuation; and the third level, at the greatest depths, further refines clustering with six subsections at 60° angles to efficiently manage data flow and energy usage. Normal nodes are positioned in the middle section for direct communication with the cluster head, whereas advanced nodes in the outer section utilize helper nodes to relay data, reducing transmission energy consumption. By adapting to environmental factors such as water pressure and attenuation, the proposed scheme effectively addresses communication challenges in deep-water UWSNs. Simulation results demonstrate that EEMLAC achieves superior performance, retaining approximately 0.6 J residual energy after 1000 rounds, reducing energy consumption to 2.3 × 10−3 J per node, extending network lifetime beyond 1800 rounds, achieving a packet delivery ratio (PDR) of 0.586, and improving throughput to 3.7 × 106 bits/s, outperforming benchmark schemes such as EBREC, EAMC, and EGRCs.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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