Design and Performance Optimization of High Efficiency Wireless Sensor Network Data Transmission Algorithm

Abstract

Wireless Sensor Networks (WSN), as the cornerstone of modern Internet of Things (IoT) technology, achieve comprehensive perception and real-time transmission of physical world information by densely deploying small and lowpower sensor nodes in target areas, greatly promoting interconnectivity between people and things, and between things. However, the limited energy and communication capabilities of sensor nodes make efficient and reliable data transmission a major challenge in WSN design in a big data environment. To address this challenge, this paper proposes an innovative WSN data transmission optimization algorithm based on Ant Colony Optimization Neural Network (ACO-NN). This algorithm combines the global search capability of ACO with the powerful learning ability of neural networks. Specifically, the algorithm utilizes ACO to explore and accumulate pheromones on different paths, while using neural networks to evaluate and predict path quality, thereby guiding the selection and optimization of data transmission paths. The experimental results show that the algorithm proposed in this paper can significantly improve the efficiency and reliability of data transmission, reduce energy consumption, and extend network lifespan.