基于预测模型的无线传感器网络节能数据采集

Balakumar D, Rangaraj J
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引用次数: 0

摘要

许多实时应用都使用了先进的无线传感器网络(wsn)。由于有限的内存、功率限制、较窄的通信带宽和较低的无线传感器节点处理单元,无线传感器网络受到严重的资源约束。无线传感器网络中的数据预测算法对于减少冗余数据传输和延长网络寿命至关重要。当数据聚合过程运行时,可以使用适当的机器学习(ML)技术来减少冗余。研究人员一直在寻找有效的建模策略和算法,以帮助从已有的WSN模型中生成高效且可接受的数据聚合方法。本文提出了一种节能的自适应海鸥优化算法(ASOA)协议,用于选择最佳簇头(CH)。利用极限学习机(extreme learning machine, ELM)选择每个节点对应的数据,生成一棵树对传感器数据进行聚类。双图卷积网络(DGCN)是一种利用时间序列数据预测未来趋势的分析方法。对每个簇头进行数据聚类和聚合,以有效地跨wsn进行样本数据预测,主要是为了最小化预测算法带来的处理开销。仿真结果表明,该方法在可靠性、数据减少和功耗方面是实用和高效的。结果表明,所提出的数据收集方法显著优于现有的最小均方(LMS)、周期数据预测算法(P-PDA)和组合数据预测模型(CDPM)方法。所提出的DGCN方法传输抑制率为92.68%,与现有方法(LMS、P-PDA和CDPM)相比分别有22.33%、16.69%和12.54%的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks
Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).
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