Distributed fuzzy clustering approach for balanced energy consumption in large-scale networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• The Internet of Things (IoT) relies heavily on wireless sensor networks (WSNs) for data transmission.
• WSNs face significant challenges in energy efficiency, latency, and battery lifespan, impacting performance.
• Uneven energy consumption in WSNs leads to premature network failure and reduced operational longevity.

Purpose of the Study:

• To address energy consumption imbalance and prolong network lifetime in WSNs.
• To propose a novel fuzzy logic-based clustering scheme for optimized cluster head (CH) selection.
• To enhance overall WSN performance by improving energy efficiency and data reliability.

Main Methods:

• Developed a multi-level fuzzy logic framework for CH selection.
• Integrated residual energy, distance to base station, and application requirements into the fuzzy system.
• Implemented unequal clustering combined with adaptive CH rotation to mitigate energy consumption hotspots.

Main Results:

• The proposed scheme increased network lifetime by approximately 45% compared to AODV and LEACH protocols.
• Achieved a 30% enhancement in energy efficiency.
• Improved the reliability of data transmission within WSNs.

Conclusions:

• The novel fuzzy logic-based clustering scheme effectively optimizes CH selection and balances energy consumption in WSNs.
• The proposed method offers a significant improvement in network lifetime, energy efficiency, and data reliability for IoT applications.
• The multi-dimensional fuzzy evaluation mechanism provides a dynamic and measurable approach to clustering performance in heterogeneous IoT environments.