Lightweight Security and Resource Management Model for IoT-Enabled Healthcare Using SDN-Based Edge Computing
Year: 2025
Type: article
Abstract: The integration of Internet of Things (IoT) technologies into healthcare systems has significantly enhanced real-time monitoring and patient-centered care, yet it also introduces substantial challenges concerning device security. Many existing frameworks focus on either enhancing security or optimizing resource management but seldom address both aspects concurrently in a lightweight manner. This study presents a novel framework that unifies lightweight device authentication using a probabilistic K-Nearest Neighbors (p-KNN) algorithm with dynamic resource management through Software-Defined Networking (SDN) controllers in edge computing environments. The framework was implemented in Python within the Jupyter Notebook environment to emulate 100 IoT healthcare devices transmitting data over a 100-second interval. Simulation-based performance evaluation revealed promising outcomes, including 100% authentication accuracy, an average latency of 73.78 milliseconds, a throughput of approximately 49,103 packets per second, a packet delivery ratio (PDR) of 98%, and an energy efficiency of 20 packets per joule. When compared to existing lightweight protocols such as the WbAES hybrid encryption framework (Irshad et al., 2023) and other lightweight key management schemes (Saif et al., 2024), the proposed model demonstrated a substantial reduction in session-level time cost while maintaining high classification accuracy, albeit with a modest increase in communication overhead. These simulation findings confirm the model’s potential suitability for healthcare applications that demand low latency, high reliability, and strong security, while also highlighting the need for future validation on physical IoT hardware and the integration of predictive traffic management mechanisms.
Authors: Halima Jabi, Umar Iliyasu, Yusuf Surajo
Institution: Federal University Dutsin-Ma
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415060413
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