Centralized two tier clustering method for wireless sensor networks based on a coupled cascaded fuzzy system

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Internet of Things (IoT) applications often use wireless sensor networks (WSNs) requiring efficient energy management.
• Battery-powered sensor nodes necessitate strategies to maximize network lifetime.
• Clustering is a key technique for energy conservation in WSNs, with cluster head selection being critical.

Purpose of the Study:

• To propose a novel centralized, two-tier clustering method for WSNs.
• To enhance network lifetime and energy efficiency through intelligent cluster head and super cluster head selection.
• To leverage soft computing, specifically fuzzy logic, for improved WSN performance.

Main Methods:

• A centralized, two-tier clustering architecture with cluster heads (CHs) and super cluster heads.
• Implementation of a sustainability filter to remove nodes with low residual energy.
• Utilization of a two-stage cascaded fuzzy system for selecting CHs and super CHs based on energy and other variables.

Main Results:

• The proposed method effectively circumvents rapid energy depletion near the base station.
• Simulations demonstrated a substantial enhancement in WSN lifetime across various scenarios.
• The approach showed a significant degree of adaptability to different base station locations.

Conclusions:

• Cascaded fuzzy systems offer a robust solution for energy management in WSNs.
• The two-tier clustering method significantly improves network longevity and performance.
• The proposed technique is adaptable and effective in diverse WSN deployment settings.