Comprehensive Analysis of Classification Algorithms in Wireless Body Area Networks: Advanced Techniques for Securing and Optimizing Trustworthy Node Identification

Israa Ibraheem Al Barazanchi Israa Ibraheem Al Barazanchi; Ali Adham

doi:10.71229/njemcs.v1i1.4

Authors

Israa Ibraheem Al Barazanchi 1College of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia
Ali Adham Iraqi Society of Engineering Management

DOI:

https://doi.org/10.71229/njemcs.v1i1.4

Keywords:

Wireless Body Area Networks,, Node Classification, , Security and Privacy,, Machine Learning, , Trustworthiness,, Energy Efficiency

Abstract

Wireless Body Area Networks (WBANs) have become essential in healthcare, fitness, and emergency monitoring, where they support real-time data collection and transmission from wearable or implantable sensors. However, the effectiveness of WBANs depends heavily on reliable and secure node classification, as these networks face numerous challenges, including data integrity issues, privacy concerns, and resource constraints. This study addresses the critical problem of accurately and securely classifying nodes in WBANs to ensure that only trusted data sources contribute to network operations, thus minimizing risks related to data breaches, energy inefficiency, and misclassification. The primary objective of this study is to analyze and evaluate various classification algorithms—ranging from traditional machine learning techniques to advanced AI models—within the context of WBANs. Additionally, this study explores methods to enhance WBAN security and optimize resource consumption, focusing on power efficiency, latency reduction, and error mitigation. Our contributions include a comparative analysis of different algorithms based on performance metrics such as accuracy, precision, recall, F1 score, energy efficiency, and robustness. We also introduce a framework for incorporating security-enhanced classification techniques, such as encryption, data anonymization, and authentication, to safeguard WBAN data and maintain network integrity. By providing a comprehensive overview of current challenges and advanced classification methods, this study aims to inform the development of scalable, secure, and efficient classification models tailored to WBANs. Our findings underscore the importance of balancing accuracy, energy efficiency, and security to create WBAN systems that are both trustworthy and adaptable. This research contributes to the ongoing evolution of WBAN technology, paving the way for improved reliability in applications that demand high standards of data integrity and real-time monitoring.

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