An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

• Autorzy: Saadallah Noor Raad , Alabady Salah Abdulghani

Identyfikatory

DOI

10.26636/jtit.2024.2.1510

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data aggregation; energy consumption; lossless compression; LZW algorithm; WSN; scheduling

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2024
• Tom: nr 2
• Strony: 48--57
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Saadallah Noor Raad

• Computer and Information Engineering Department, College of Electronics Engineering, Ninevah University, Mosul, Iraq

• https://orcid.org/0000-0002-6012-2910

autor

Alabady Salah Abdulghani

• Computer and Information Engineering Department, College of Electronics Engineering, Ninevah University, Mosul, Iraq

• https://orcid.org/0000-0001-9687-2724

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