International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 961KB)
Similarity-Navigated Graph Neural Network: Energy-Efficient De-Duplication for Healthcare Data Aggregation in IOT Environments
PDF (961KB), PP.46-63
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Author(s)
Aishwarya Shekhar
1
Abdul Aleem
2,*
Index Terms
De-duplication, Energy Transformer, Fog Server, Human Memory Optimization, Schoof’s Dynamic Elliptic Curve Cryptography
Abstract
The rapid expansion of the Internet of Things (IoT) has enabled real-time patient monitoring through medical sensors, but redundant readings significantly increase storage, transmission, and energy costs. To address this challenge, we propose the Energy-Schoof’s Cryptography-based Similarity-Navigated Graph Neural Network with Human Memory Optimization (ESC-SNGNNet-HMO) for healthcare data aggregation. The framework efficiently manages data chunks by combining deduplication, energy-aware processing, and secure transmission. At the fog layer, a Similarity-Navigated Graph Neural Network (SNGNN) identifies duplicate records, with hyperparameters optimized through Human Memory Optimization (HMO) to enhance accuracy. Deduplicated data is then securely transferred to the cloud using Schoof’s Dynamic Elliptic Curve Cryptography (SDECC). Experimental evaluation demonstrates that ESC-SNGNNet-HMO maintains a throughput of 230 KB/s even with 8% packet loss, reduces storage to as little as 14 bytes, and eliminates up to 99% of duplicate data in low-node scenarios. Overall, the system provides an energy-efficient and cyber-secure solution for redundancy management in IoT-based healthcare applications.
Cite This Paper
Aishwarya Shekhar, Abdul Aleem, "Similarity-Navigated Graph Neural Network: Energy-Efficient De-Duplication for Healthcare Data Aggregation in IOT Environments", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.4, pp. 46-63, 2026. DOI:10.5815/ijcnis.2026.04.03
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