AI-integrated adaptive MANET framework for IoT-driven healthcare systems: enhancing scalability, security, and real-time communication

Abstract

The increasing reliance on real-time, reliable communication in healthcare-focused IoT environments has amplified the importance of secure and adaptive Mobile Ad Hoc Networks (MANETs). Traditional MANET routing protocols, such as AODV, DSR, and OLSR, often fall short in addressing the dynamic nature of healthcare applications due to their limited adaptability, lack of integrated security, and insufficient Quality of Service guarantees. Existing machine learning-based solutions provide partial improvements but frequently overlook trust modeling and energy efficiency in highly mobile or resource-constrained environments. To address these challenges, this paper proposes HealthMANET-AI, an AI-integrated adaptive MANET framework for IoT-driven healthcare systems, centered around a novel model called MedRouteNet. MedRouteNet utilizes Q-learning-based reinforcement learning to dynamically determine optimal routing paths, incorporating behavior-based trust evaluation and quality of service constraints, including latency, delivery ratio, and energy consumption. The model adapts to network changes in real-time, penalizes misbehaving nodes, and enhances data delivery reliability in hostile or unstable conditions. Experimental evaluation using NS-3 and PyTorch shows that HealthMANET-AI outperforms conventional protocols and baseline models in packet delivery ratio (by up to 18%), reduces average delay and jitter, and achieves 92.6% F1-score in malicious node detection. These results validate the robustness, scalability, and effectiveness of the proposed framework in ensuring secure, low latency, and energy-efficient communication, making it highly suitable for mission-critical applications such as remote patient monitoring, mobile diagnostics, and emergency healthcare response. The proposed model offers a substantial advancement toward intelligent, secure, and context-aware MANETs for next-generation IoT healthcare systems.