Advancing Security: Exploring AI-driven Data Encryption Solutions for Wireless Sensor Networks
L Arulmurugan, Sangeeta Thakur, R. Dimple Dayana, S. Thenappan, Banavath Nagesh, R. Kalaivani Sri · 2024
The research thoroughly examines encryption techniques in Wireless Sensor Networks (WSNs), emphasizing machine learning-based, conventional, and cutting-edge AI-driven approaches. Important conclusions are drawn from a comparative analysis of speed, overhead, and energy use. The machine learning approach shows significant flexibility and increased security, whereas the conventional approaches show subtle trade-offs between energy use and network latency. This study presents a revolutionary AI-driven encryption architecture and reveals improved performance measures, such as reduced overhead, maximum energy efficiency, and faster speed. The outcomes demonstrate how AI can completely transform WSN security by providing a flexible and reliable solution. Graphics improves the findings’ comprehensibility and offers a more sophisticated view of performance differences. This study establishes a standard for current practices and opens the door for further developments in WSN security through AI integration.