Modernizing Wireless Smart Connect Ecosystems through Oracle Cloud Databases and Machine Learning: A Comparative Security Framework for Image Denoising Efficiency
DOI:
https://doi.org/10.15662/IJARCST.2024.0706007Keywords:
Wireless Smart Connect Ecosystem, Image Denoising, Oracle Cloud Databases / OCI, Machine Learning / Deep Learning, Comparative Framework, PSNR / SSIM Metrics, Latency & Resource Usage, Edge vs Cloud Processing, Data Ingestion & Storage, Efficiency‐Quality Trade offAbstract
Wireless smart‑connect ecosystems involve interconnected devices, sensors, and communication networks that continuously capture, transmit, and use image data. In many applications—surveillance, environmental monitoring, autonomous vehicles, remote health diagnostics, etc.—these images suffer from noise caused by low light, sensor imperfections, compression artifacts, wireless channel distortion, or interference. Efficient image denoising is thus critical to maintain data quality, reduce downstream errors, and optimize bandwidth usage. This paper proposes a comparative framework that leverages Oracle Cloud Databases (on Oracle Cloud Infrastructure, OCI) plus machine learning models to evaluate and improve image denoising efficiency in wireless smart‑connect ecosystems.
The framework consists of several layers: wireless image capture; ingestion and storage of noisy image data into Oracle Autonomous Database; preprocessing; training and comparing various machine learning / deep learning based denoising models (such as CNNs, autoencoders, self‑/semi‑supervised methods); measuring performance in terms of denoising quality (e.g. PSNR, SSIM), latency (processing time), resource usage (compute, memory), and data transfer costs. A comparative evaluation is conducted between models deployed on different configurations (on‑cloud vs edge), different sizes of wireless networks, and different noise profiles.
Results show that while high‑capacity deep models achieve better denoising quality, their latency, cost, and resource consumption increase substantially. Oracle’s managed database services facilitate efficient storage and retrieval, versioning, and metadata management for images and models, but some bottlenecks exist in data transfer and query latency for large volume image data. The framework finds trade‑offs, e.g. selecting lighter models or performing partial denoising at edge vs full denoising in cloud. The study concludes that integrating Oracle Cloud Databases with machine learning models in a wireless smart‑connect ecosystem can significantly enhance denoising efficiency, but careful system design is essential. Policy implications include optimizing cost, latency, energy and ensuring data security/privacy.
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