Optimizing Secure and Efficient Data Aggregation in IoMT Using NSGA-II - Maria Zuraiz; Muhammad Javed; Nasim Abbas; Waseem Abbass; Waqas Nawaz; Ashfaq Hussain Farooqi (IEEE Access, 2025)
The Internet of Medical Things (IoMT) enables real-time healthcare monitoring but faces challenges in energy efficiency, latency, security, and scalability. This paper presents a novel and comprehensive hierarchical data aggregation framework for the Internet of Medical Things (IoMT), optimized using a constrained multi-objective approach. Unlike existing methods that treat objectives in isolation, the model simultaneously minimizes energy consumption, latency, security risks, and authentication overhead under real-world constraints (500 mJ energy, 200 ms latency, 0.9 Sybil detection rate, 0.95 data integrity). Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a diverse Pareto front of solutions is generated, allowing adaptability to different healthcare scenarios. The framework is further validated through dual-environment simulations (MATLAB and NS-3) under realistic attack conditions (Sybil, spoofing, and DoS), demonstrating superior performance and robustness compared to centralized and fog-only baselines. For instance, the system achieves up to 43% energy savings and 84% latency reduction. These results illustrate the framework’s practical viability for scalable, secure, and efficient IoMT deployments.