A Simulated Smart Patient Room: Dynamically Allocating Bandwidth To Wearable Sensor’s Critical Data For Real-Time Health Monitoring By Integrating Iot, Telemetry And Edge Computing

Abstract

In the current healthcare era, a lot of network traffic data is generated via the Internet of Things (IoT) devices and wearable IoT sensor-based devices. An efficient and fast management of the high priority vitals data generated from these devices is crucial for the health monitoring of a critical patient. In remote places, health monitoring could be a challenge due to limited bandwidth and unreliable internet connectivity.

This paper proposes a dynamic traffic prioritization scheme for healthcare IoT networks using software-defined networking (SDN) principles. The proposed model leverages the Ryu controller's capabilities to intelligently manage traffic flow, prioritize critical health data while minimizing the transfer of non-urgent data and allocate bandwidth dynamically based on the criticality of the transmitted information. The model incorporates a queue size estimation algorithm utilizing Exponential Weighted Moving Average (EWMA) for high-priority queues and simple averaging for medium and low-priority queues. Queue sizes drive weight adjustments, where weights represent the relative importance of each traffic category. Bandwidth allocation is dynamically computed based on these weights, ensuring that critical health data receives higher priority over non-urgent traffic. Proactive adjustments, such as traffic prioritization and dynamic bandwidth allocation enhances the system's responsiveness and adaptability to changing network conditions.