What is it about?
Wireless Sensor Networks (WSNs) are broadly utilized in applications such as smart cities, environmental watching and military observation, where energy-efficient and secure data transmission is necessary. However, restricted sensor node resources and malicious attitudes create important challenges to reliable transmission. This work introduces fuzzy-based cluster head selection with ensemble learning for enhancing confidentiality (FCEC) in WSNs.
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Why is it important?
The goal is to optimize security procedures by eliminating vulnerabilities and increasing confidentiality. This system uses a fuzzy logic system (FLS) to select the efficient cluster head (CH) for improving energy efficiency. A FLS contains fuzzy input and output variables. The node degree, distance from sensor to base station (BS), and remaining energy parameters are applied as the inputs, and the output is the round length. The better round length sensor node is picked out of the CH, and it increases the network lifetime.
Perspectives
The simulation outcomes show that the proposed FCEC mechanism improves the packet delivery ratio and residual energy performance compared to the conventional mechanism. Furthermore, it increases the cluster round and reduces the network delay.
Dr Velmurugan S
TJS Engineering College
Read the Original
This page is a summary of: Fuzzy-based Cluster Head Selection with Ensemble Learning for Enhancing WSN Confidentiality, July 2026, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/iccmc69250.2026.11624732.
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