Enhancing TEEN Protocol Using Sectored KNN-Genetic-Fuzzy Clustering, Load Balancing, and Lifetime Optimization in WSNs

Wireless Sensor Networks (WSNs) are increasingly utilized for environmental monitoring, industrial control, and smart infrastructure because of their distributed nature and low-power capabilities. However, energy efficiency remains a fundamental challenge due to limited battery capacity and uneven energy consumption. This paper proposes SKNN-GFL-TEEN, a novel hybrid framework that integrates Sector-based K-Nearest Neighbors (SKNN) with Genetic-Fuzzy Logic (GFL) under the Threshold-sensitive Energy Efficient Network (TEEN) protocol to enhance network lifetime and stability. The SKNN model partitions the network into distinct sectors and selects candidate nodes based on spatial proximity. Simultaneously, the GFL mechanism further refines cluster head (CH) selection through genetic optimization and fuzzy rule-based evaluation. Simulation results demonstrate that SKNN-GFL-TEEN outperforms conventional protocols, including LEACH, TEEN, and PEGASIS, in terms of network lifetime, energy consumption, and data delivery rate. Moreover, the proposed method demonstrates improved adaptability, scalability, and robustness, making it suitable for real-time and large-scale WSN deployments.