Leveraging Levy Flight and Lotus Effect for Secure Routing and Anomaly Detection in Wireless Sensor Network

ABSTRACT The rapid increase in the integration of wireless sensor networks within the Internet of Things (IoT) ecosystem has led to crucial difficulties in providing reliable, energy‐efficient, and secure communication. Most of the traditional intrusion detection models face few struggles in mitigating these challenges due to limited scalability and centralized processing. Therefore, this paper proposes a lightweight federated learning–based energy‐aware (LF‐LEA) model to overcome all the existing issues. The proposed model is an integration of federated learning, bio‐inspired optimization, and energy‐aware routing for decentralized environments. For local intrusion detection at edge nodes, the proposed model uses a lightweight convolutional neural network to transmit only the model updates rather than raw data, and this ensures the user's data privacy. The integration of the Levy flight and lotus effect mechanisms enhances the exploration and exploitation balance for improved intrusion detection accuracy. Furthermore, the S‐LEACH–based routing protocol is incorporated to ensure secure and energy‐efficient communication between nodes and base stations. Two benchmark datasets are used to validate the proposed model. The experimental results demonstrated that the proposed model achieves a higher accuracy of 98.60%, a precision of 98.32%, and a packet delivery ratio of 92.7%. In addition, the proposed model achieves a minimum communication delay and false alarm rate. Furthermore, the statistical Wilcoxon rank‐sum test is conducted to confirm the effectiveness and consistency of the proposed model across diverse evaluation metrics. The overall result demonstrates that the proposed model ensures privacy preservation, scalability, and energy efficiency, making it a robust model for real‐time intrusion detection in IoT‐enabled applications, including smart cold storage monitoring systems, industrial automation, and environmental sensing networks.