Binarized simplicial convolutional neural networks for secure and energy-efficient data aggregation in WSN

In Wireless Sensor Networks (WSNs), secure and energy-efficient data aggregation has been challenging due to limited energy supply, dynamic nature of the network, and presence of malicious nodes. In this paper, a Binarized Simplicial Convolutional Neural Networks for Secure and Energy-Efficient Data Aggregation in WSN (CPGIMC-SEIDA-WSN) is proposed. Initially, WSN nodes are collected from real-time environmental data like humidity, temperature and motion. The Consensus Partition Guided Incomplete Multi-View Clustering (CPGIMC) used to format the clusters and selects the optimal CH through Parameter Adaptive Manta Ray Foraging Optimization (PAMRFO). Malicious nodes are identified and isolated using novel Binarized Simplicial Convolutional Neural Networks (BSCNN), which analyze the topological relationships among sensor nodes and detect abnormal communication patterns to ensure data integrity. The Superb Fairy-Wren Optimization Algorithm (SFOA) also optimizes the path of the data transmission through three parameters like trust, connectivity and quality of service (QoS). The simulation proves that the proposed approach has the capability to provide 23.55%, 15.31% and 45.33%higher Packet delivery Ratio; 43.36%, 41.36% and 23.44% higher Network lifetime; 22.24%, 34.11% and 16.14% lower delay than the existing methods such as AGNN-CHR-SDA-WSN, EDT-EEC-IDSR-WSN and WSNL-IC respectively.