Optimizing Data Survivability in Unattended Wireless Sensor Networks: A Machine Learning Approach to Cluster Head Selection and Hybrid Homomorphic Encryption - Sivaraman, Haritha K; L, Rangaiah (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2025)
The research relies on machine learning-based Cluster Head (CH) selection and optimised Attribute-Based Encryption (ABE) with Homomorphic Encryption to improve data survivability in Unattended Wireless Sensor Networks (UWSNs). Integrating blockchain technology would enable tamper-proof data storage and provenance. The suggested method uses machine learning techniques like Deep Q-Networks (DQNs) or other models for intelligent and adaptive CH selection in UWSNs. Dynamically selecting CHs takes into account energy efficiency, network coverage, communication dependability, and node characteristics. The second part protects data using optimised Attribute-Based Encryption (ABE) and Homomorphic Encryption. ABE offers fine-grained attribute-based access control to restrict data access to authorised entities. Secure processing of encrypted data using homomorphic encryption protects privacy and integrity. These encryption algorithms are optimised to balance security and computational performance for efficient data processing and transmission while guaranteeing data privacy and integrity. Blockchain technology is suggested for tamper-proof data storage and provenance. To optimise the suggested solution's performance, the study uses the Seagull Optimisation Algorithm (SOA) and the Whale Optimisation Algorithm (WOA). These algorithms fine-tune system parameters, optimise CH selection, and boost UWSN performance. This holistic strategy uses machine learning-based CH selection, optimised ABE with Homomorphic Encryption, and blockchain technology for tamperproof data storage and provenance to improve UWSN data survival. Optimisation algorithms boost the solution's efficacy and efficiency, protecting UWSN data, latency, and energy usage.