An Internet of Things‐Based Wireless Sensor Network Secure Routing and Monitoring System Using Deep Learning With Hybrid Optimization

ABSTRACT As the Internet of Things (IoT) drives global smart networks, secure, efficient, and resilient wireless sensor networks (WSNs) are critical. The existing methods often fail to balance energy efficiency, real‐time adaptability, and robust protection against advanced threats. This manuscript introduces an innovative approach to enhancing security and efficiency in IoT‐driven WSNs by proposing an adaptive energy‐efficient balanced uneven clustering (AEBUC) routing protocol integrated with attention‐guided generative adversarial networks (AG‐GAN). Addressing critical gaps in current research, the AEBUC protocol efficiently monitors sensor nodes, identifying potential adversaries while a path‐oriented data encryption model strengthens security by selecting sensor guard nodes. The use of AG‐GAN optimizes the selection of sensor monitor nodes and determines the most secure routes for encrypted data transmission. Furthermore, the improved border collie optimization (IBCO) algorithm fine‐tunes AG‐GAN's weight parameters, ensuring optimal performance. Implemented in Python and evaluated against key performance indicators such as network lifetime (NL), packet delivery ratio (PDR), throughput, delay, and encryption time (ET), the proposed model achieves 92% higher PDR, 14.4 s lower delay, and 6 s lower ET. The significance of this work lies in its comprehensive solution, combining adaptive clustering with advanced GAN‐based security, making a substantial impact on the reliability and safety of WSNs in IoT environments.