Deep Learning-Infused Hybrid Security Model for Energy Optimization and Enhanced Security in Wireless Sensor Networks (2024-09-02)

TL;DR

This study employs additional optimization and a deep learning approach to identify and isolate a rogue node from the busiest one based on various criteria, and achieves high throughput, reduced processing time, and a significantly lower packet loss rate, with nearly a threefold decrease in the percentage of missing packages.

Abstract

Many wireless sensors are placed ad hoc to form a wireless sensor network (WSN), monitoring system, physical, and environmental conditions. Base stations and nodes constitute the system. The WSN's base station connects to the Internet, facilitating data sharing. These networks cooperatively transfer data to the base station while monitoring factors like sound, pressure, and temperature. The collected data undergo processing, analysis, storage, and mining. This study employs additional optimization and a deep learning approach to identify and isolate a rogue node from the busiest one based on various criteria. The deep learning model calculates probabilities using a sum-rule weighted method for request forwarding, reply forwarding, and data dropping. The planned task exhibits high throughput and reduced necessary time. Packet loss rates have decreased, with delay-related hyper metrics dropping from 70 to 42 ms. The percentage of missing packages has nearly threefold reduced from 23 to 8%. The adoption of deep learning eliminates hostile node behaviour, mitigating potential network failures. Enhanced Study optimizes with deep learning to pinpoint rogue nodes in the busiest, using sum-rule weighted probabilities for request and reply forwarding, and data dropping. A deep learning model uses sum-rule weighting to compute probabilities for request handling, reply forwarding, and data dropping. This ensures high throughput and minimizes processing time in the planned tasks. The proposed work achieves high throughput, reduced processing time, and a significantly lower packet loss rate, with nearly a threefold decrease in the percentage of missing packages. Enhanced Study optimizes with deep learning to pinpoint rogue nodes in the busiest, using sum-rule weighted probabilities for request and reply forwarding, and data dropping. A deep learning model uses sum-rule weighting to compute probabilities for request handling, reply forwarding, and data dropping. This ensures high throughput and minimizes processing time in the planned tasks. The proposed work achieves high throughput, reduced processing time, and a significantly lower packet loss rate, with nearly a threefold decrease in the percentage of missing packages.

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Enhanced Study optimizes with deep learning to pinpoint rogue nodes in the busiest, using sum-rule weighted probabilities for request and reply forwarding, and data dropping