Two-Level Fuzzy Logic for Cluster Head Selection in Wireless Sensor Networks

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Resource Overview

This study presents a cluster head selection methodology utilizing a two-level fuzzy logic system, designed to optimize data transmission and traffic management in wireless sensor networks. The approach incorporates code-level implementation insights, including fuzzy rule base design and membership function configuration.

Detailed Documentation

This article introduces a cluster head selection method employing two-level fuzzy logic, which enhances the efficiency of selecting optimal cluster head nodes for improved data transmission and traffic control in wireless sensor networks. We delve into the operational mechanism of this approach, supported by algorithmic explanations such as the dual-stage fuzzy inference process (comprising input fuzzification, rule evaluation, and defuzzification) and key functions like calculate_fuzzy_score() for node evaluation. Performance assessment metrics including energy consumption analysis and network lifetime simulation are examined to demonstrate practical applicability. Furthermore, we explore potential implementations—such as integrating dynamic threshold adjustments via update_fuzzy_rules() function—and future research directions like machine learning-enhanced fuzzy systems to advance this field.

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