Effectual Energy Optimization, Fault-Tolerant Attack Detection, and Data Aggregation in Healthcare IoT Using Enhanced Waterwheel Archimedes and Deep Siamese Maxout Forward Harmonic Networks
DOI:
https://doi.org/10.18196/jrc.v6i2.25181Keywords:
Internet of Medical Things, Cluster Head Selection, Secure Routing, Siamese Neural Network, Deep Maxout NetworkAbstract
The Internet of Medical Things (IoMT) has emerged as a transformative technology for improving healthcare delivery and patient outcomes. However, IoMT systems face significant challenges, including high latency, energy inefficiency, and vulnerability to cyberattacks, which compromise data security and patient privacy. Existing methods for attack detection and secure routing in IoMT often suffer from high latency, limited fault tolerance, and insufficient accuracy in identifying sophisticated attacks. To address these challenges, this paper proposes two novel approaches: the Improved Waterwheel Archimedes Optimization Algorithm (WWAOA) for secure routing and the Deep Siamese Maxout Forward Harmonic Network (DSMFHN) for attack detection in healthcare IoT. The Improved WWAOA integrates the Waterwheel Plant Algorithm (WWPA) with the Archimedes Optimization Algorithm (AOA) to optimize cluster head (CH) selection and secure routing. It considers key fitness parameters such as energy consumption, link lifetime (LLT), trust, delay, distance, and fault tolerance to enhance network efficiency and resilience. The DSMFHN combines Siamese Neural Networks (SNN) and Deep Maxout Networks (DMN) with forward harmonic analysis to detect attacks with high accuracy and low false positive rates. Additionally, data aggregation is performed using Bidirectional Long Short-Term Memory (BiLSTM) with adaptive weightage based on fault and malicious node detection. Experimental results demonstrate that the proposed methods outperform existing techniques. The Improved WWAOA achieves a minimal delay of 0.557 ms, maximal energy efficiency of 0.182 J, a packet delivery ratio (PDR) of 93.894%, and a trust value of 87.152. Meanwhile, the DSMFHN achieves a high accuracy of 92.598%, a true positive rate (TPR) of 91.643%, and a low false positive rate (FPR) of 0.156. These results highlight the effectiveness of the proposed methods in addressing the critical challenges of latency, energy efficiency, and security in healthcare IoT systems.
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