A Comprehensive Survey: Energy-Efficient, Cluster-Based Routing Protocol For Wireless Sensor Networks Using Fuzzy Logic

Abstract

Wireless Sensor Networks (WSNs) are essential for many applications, such as smart cities, healthcare, and environmental monitoring. However, because sensor nodes have limited power resources, energy efficiency and network longevity continue to be major concerns. Fuzzy-based Routing and clustering protocols have been created to overcome these difficulties, using fuzzy logic to improve data transmission and cluster formation decision-making. This survey demonstrates the conventional methods as well as the cutting-edge hybrid approaches in WSN. In addition, it highlights the integration of fuzzy logic with other advanced techniques like machine learning, genetic algorithms, and swarm intelligence, which aim to further enhance energy efficiency, scalability, and overall network performance. By exploring the strengths and limitations of these hybrid models, the survey provides valuable insights into the future direction of WSN research, emphasizing the need for adaptive, intelligent systems capable of addressing the evolving challenges of real-world applications.

Introduction

Wireless sensor networks (WSNs) are widely used in automation, healthcare, surveillance, and monitoring, but energy consumption remains a major challenge impacting their performance and lifespan. To address this, routing and clustering techniques are critical for optimizing energy use.

Clustering groups sensor nodes to improve scalability and reduce energy use. Key clustering algorithms include:

• LEACH: Randomly selects cluster heads (CHs) but suffers from uneven energy distribution.

• HEED: Improves stability by choosing CHs based on residual energy and communication cost, though it adds complexity.

• DEEC: Dynamically selects CHs based on residual energy, extending network life.

Routing protocols determine efficient data paths:

• PEGASIS: Chain-based to reduce transmission distance but has high latency.

• TEEN: Suitable for reactive networks but less ideal for periodic data collection.

• LEACH-C: Centralized CH selection to enhance energy efficiency.

Fuzzy logic is applied to manage uncertainty in WSNs and improve decision-making for CH selection and routing by considering multiple factors like distance, node density, and energy levels. Combining fuzzy logic with optimization algorithms such as Quantum Particle Swarm Optimization (QPSO) enhances energy balance, load distribution, and network lifetime.

Related research highlights:

• Traditional clustering protocols like LEACH have limitations in scalability and energy distribution.

• Metaheuristic algorithms (PSO, GA) optimize routing/clustering but face premature convergence issues, addressed by QPSO.

• Hybrid fuzzy logic and QPSO approaches outperform traditional methods by improving energy efficiency and prolonging network life.

• Future work should focus on scalability, integration with energy harvesting, security, hybrid optimization with machine learning, and real-time adaptability.

Conclusion

Clustering approaches utilizing fuzzy logic may be able to create optimal clusters that reduce total energy usage during operation. A number of researchers have been working for the past few years to build an efficient clustering algorithm for WSNs utilizing fuzzy logic after realizing this fact. In WSNs, fuzzy logic-based techniques increase scalability, adaptability, and energy efficiency. These techniques allow the network to handle dynamic conditions, such as node mobility and varying environmental factors, while maintaining high performance. By providing a more flexible and robust approach to clustering and routing, fuzzy logic can effectively balance energy consumption and network stability, leading to prolonged network lifespans. Furthermore, as technology continues to evolve, the integration of fuzzy logic with emerging technologies, such as machine learning and AI, holds great promise for developing even more sophisticated algorithms that can intelligently manage energy resources in real-time. This evolution could potentially revolutionize WSNs in a wide range of applications, from smart cities to healthcare, by ensuring that the networks are not only efficient but also capable of supporting the growing demands of the Internet of Things (IoT). Ultimately, the continued research and refinement of fuzzy logic-based techniques will be crucial in advancing WSNs to meet the challenges of next-generation applications.

References

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Copyright

Copyright © 2025 Mohanraj S, Dr V Sumathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.