Abstract

The management of Wireless Sensor Networks (WSNs) poses significant challenges, primarily due to the constraints on resources and the need for efficient data transmission. This paper introduces a novel approach combining random forest (RF) and fuzzy c-means (FCM) (RF-FCM) to optimize cluster head selection in WSNs. Our methodology enhances network management by effectively grouping sensor nodes based on their environmental and operational data and then applying a machine learning model to predict the most suitable cluster heads. The primary motivation is to improve network lifetime and efficiency by reducing energy consumption and balancing the load across the network. Challenges such as ensuring reliable data synchronization and maintaining robust communication amidst resource limitations are addressed. The RF-FCM model leverages historical performance data and real-time sensor information, ensuring adaptive and scalable network operations. Our results demonstrate significant improvements in network performance metrics such as energy efficiency, network lifetime, and data throughput compared to traditional methods.

Keyword

Wireless sensor networks, Cluster head selection, RF-FCM, Network management, Energy efficiency.

Cite this article

Kumar P, Patel MK.Optimizing cluster head selection in wireless sensor networks using RF-FCM. ACCENTS Transactions on Image Processing and Computer Vision. 2024;10(27):7-13. DOI:10.19101/TIPCV.2024.1026001

Refference

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