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.

Cloud computing forms the basis for the emerging technologies in various fields, providing a reliable framework for managing resources to meet the needs of different applications. The rapidly increasing energy requirements inherent to cloud computing pose a real problem concerning sustainability. Energy efficiency, fair resource sharing, and performance consistent across the dynamic and heterogeneous cloud computing system are essential since existing approaches introduce inefficiency, energy consumption, and unfair distribution of loads. This research introduces Adaptive Osprey-Bowerbird Optimized Green Cloud Computing with Randomized Attention Coupled Fair Resource Distribution in Scalable Systems (AO-BO-RNCN-MAN) to address these challenges. The proposed framework integrates the Randomized Neural Coupling Network to learn diverse data representations, with the Multi-instance Attention Network to prioritize tasks, and Adaptive Osprey-Bowerbird Optimization, which is a combination of the Osprey Adaptive Algorithm and the Adaptive Bowerbird Optimization for further fine-tuning of the system. By optimizing the placement of virtual machines and scheduling of tasks, the proposed framework guarantees fairness and high utilization of energy with low turnaround time. Performance assessments indicate that the proposed framework outperforms the existing systems with energy efficiency of 99.82%, precise task scheduling of 99.61% and fair resource allocation of 99.74%. AO-BO-RNCN-MAN not only proposes a new way of addressing green computing challenges but also opens the gates to sustainable, adaptive, and scalable designed cloud infrastructures for resource management in cloud ecosystems and establishes the proposed conceptual framework as a new standard.