一种能效高、可扩展的 WSN,具有更高的数据聚合精度

Q4 Engineering
N. Saadallah, Salah Abdulghai Alabady
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引用次数: 0

摘要

本文介绍了一种结合 K-means 聚类遗传算法(GA)和 Lempel-Ziv-Welch 压缩技术(LZW)的方法,以提高无线传感器网络(WSN)中的数据聚合效率。这项研究的主要目标是降低能耗、提高网络的可扩展性和数据聚合的准确性。此外,还采用了 GA 技术,通过选择簇头来优化簇的形成过程,同时采用 LZW 压缩聚合数据,以减少传输开销。为进一步优化网络流量,引入了有助于数据包从传感器传输到簇头的调度机制。这项研究的结果将有助于推进 WSN 中数据聚合的数据包调度机制,以减少从传感器到簇头的数据包数量。仿真结果证实,与 LEACH、M-LEACH、多跳 LEACH 和 sLEACH 方法所依赖的其他压缩方法和非压缩方案相比,该系统非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy
This paper introduces a method that combines the K-means clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system's effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches.
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来源期刊
Journal of Telecommunications and Information Technology
Journal of Telecommunications and Information Technology Engineering-Electrical and Electronic Engineering
CiteScore
1.20
自引率
0.00%
发文量
34
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