Hardware-Accelerated Cross-Layer Communication Optimization for Intelligent Continuous Glucose Monitoring Systems

Continuous Glucose Monitoring (CGM) systems have become essential for real-time diabetes management; however, ensuring reliable, energy-efficient, and low-latency communication under dynamic patient mobility remains a major challenge for intelligent healthcare networks. This paper proposes a cross-layer energy- and mobility-aware communication framework for CGM systems that integrates adaptive routing analysis, Machine Learning (ML)-based glucose prediction, and hardware acceleration within a unified architecture. The proposed framework evaluates the impact of network behavior on ML accuracy by jointly analyzing throughput, delay, Packet Delivery Ratio (PDR), energy consumption, and mobility adaptation in static and dynamic healthcare scenarios. In addition, the framework is implemented on a Nexys A7-100T FPGA platform to validate real-time feasibility through a hardware-integrated routing and ML inference architecture optimized for edge healthcare applications. Experimental results obtained using NS-3 simulation and FPGA-based validation demonstrate that the proposed framework achieves up to 25% lower energy consumption, improved communication reliability, reduced latency, and enhanced ML prediction accuracy under mobile conditions. The hardware implementation further demonstrates efficient resource utilization and low-latency processing suitable for wearable and edge-based medical devices. The results show interdependence between communication reliability and ML performance, highlighting the importance of energy- and mobility-aware cross-layer optimization for scalable, reliable, and intelligent CGM healthcare systems.