Enhancing privacy preservation and integrity in IoT-enabled wireless sensor networks through novel advanced cryptographic techniques

This study presents a novel hybrid cryptographic model designed to enhance privacy preservation and data integrity in IoT-enabled Wireless Sensor Networks (WSNs). Traditional algorithms such as RSA, AES, and Blowfish are evaluated and combined into a Hybrid Model to address the resource-constrained nature of IoT devices. The proposed model was tested on a dataset of sensor data, with performance metrics including encryption/decryption time, security strength, memory usage, data throughput, and communication overhead. Numerical findings demonstrate the Hybrid Model's superior performance, with encryption time reduced by 18% compared to Advanced Encryption Standard (AES), The hybrid model employs RSA-2048 (112-bit security strength) for key exchange and AES-256/Blowfish for data encryption (256-bit confidentiality protection). The memory usage was optimized, requiring only 25.16 KB, making it suitable for low-power IoT devices. Additionally, the Hybrid Model achieved a data throughput of 24.89 KB/s and reduced communication overhead to 1.32 KB. These results highlight the efficiency and robustness of the Hybrid Model in securing IoT-enabled WSNs. This research contributes a scalable, resource-efficient solution for privacy and data integrity, offering a promising advancement for real-time IoT applications in sectors such as healthcare, industrial automation, and smart homes.