Optimizing Data Survivability in Unattended Wireless Sensor Networks - A Machine Learning Approach to Cluster Head Selection - Optimized ABE with Homomorphic Encryption and Blockchain

Aug 4, 2026

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Machine learning-driven cluster head selection and optimized cryptography are used to improve data survivability in unattended wireless sensor networks (UWSNs). The approach employs Deep Q-Networks (DQNs) and related models to adaptively choose cluster heads using energy efficiency, coverage, communication dependability, and node characteristics. Data protection combines optimized attribute-based encryption (ABE) with homomorphic encryption to ensure privacy-preserving processing and fine-grained attribute access control. Seagull and Whale optimization algorithms tune system parameters for better performance, while blockchain is proposed for tamper-proof storage and provenance.

LanguageEnglish

CategoryResearch & Report

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Pages19

Reading Time48 min

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Article Info Abstract

Introduction

Optimizing Data Survivability in Unattended Wireless Sensor Networks: A Machine Learning Approach to Cluster Head Selection and Hybrid Homomorphic Encryption Haritha K Sivaraman1,*, 2Rangaiah L2 1,*Research Scholar, Department of Electronics & Communication Engineering, VTU, Belagavi, Raja Rajeswari College of Engineering, Bangalore, India. 2Professor, Department of Electronics & Communication Engineering, Raja Rajeswari College of Engineering, Bangalore, India. | Article history:
Received Oct 25, 2024 Revised Feb 1, 2025 Accepted Feb 26, 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.
Copyright © 2025 Institute of Advanced Engineering and Science.
All rights reserved. | | --- | --- | | Keyword:
UWSN;
Data Survivability;
Deep Q-Networks;
optimized hybrid homomorphic encryption;
Seagull optimization
Algorithm;
Whale Optimization Algorithm. | | Corresponding Author: Haritha K Sivaraman, Department of Electronics & Communication Engineering, VTU, Belagavi, Raja Rajeswari College of Engineering, Bangalore, India. [Email: haritharesearchscholar@gmail.com](Email: haritharesearchscholar@gmail.com) Article Info ABSTRACT 1. INTRODUCTION Wireless Sensor Networks (WSNs) have become the most emerging technology in the field of research for the advanced development of digital networks in recent years. WSN constitutes an extensive collection of sensors, forming a distributed network for sensing, self-organization, and data propagation [1] . In a distributed environment, WSN nodes serve as compact, self-contained devices with limited resources. They play a crucial role in processing information, communication, and sensing mechanisms, enabling the detection of environmental conditions in their immediate surroundings. These networks rely on batteries as their energy source [2] . The effectiveness of sensor nodes is limited by several factors, including storage capacity, processing speed, battery life, and more [3] . As a result, ensuring adequate security provisions [4] becomes an undertaking challenge. In the WSN, the sensors generate data and operate in a multi-hop fashion, relaying it from one node to another. Their primary objective is to efficiently gather a relevant set of information and relay it to the

FAQs

What main problem does the study address in unattended wireless sensor networks?

The study targets the survivability problem, aiming to keep the network capable of completing its mission within a timeframe despite intrusions, attacks, accidents, and failures.

How does the proposed method improve cluster head (CH) selection?

It uses machine learning techniques such as Deep Q-Networks to adaptively and dynamically select CHs based on energy efficiency, network coverage, communication dependability, and node characteristics.

What encryption approach protects data confidentiality and integrity?

The solution protects data using optimized attribute-based encryption (ABE) for attribute-level access control, together with homomorphic encryption for privacy-preserving and integrity-preserving processing of encrypted data.

How are algorithm parameters optimized to enhance performance?

Seagull Optimization Algorithm (SOA) and Whale Optimization Algorithm (WOA) are employed to fine-tune system parameters, optimize CH selection, and improve overall UWSN performance, including latency and energy usage.

Aug 4, 2026

Upload by Levi