Queue-Based Analytical Modeling for Capacity Estimation of Wearable eHealth Systems

The heterogeneity in IoT-based health monitoring devices and communication protocols leads to diverse system designs and configurations resulting in uncertain outcomes. Most present-day eHealth approaches focus on optimizing the performance of already designed systems (hardware, network, and software) rather than optimizing the system design itself. A reliable eHealth monitoring system must use appropriate protocols and hardware to avoid data loss and delays. Identifying the magnitude of performance changes when integrating new devices into an already functional eHealth system is also critical. This work addresses these issues by adopting a queuing theory-based analytical model to characterize an eHealth wearable system. This approach acts as a guiding framework for system and network capacity estimation by simulating the dynamics of an eHealth wearable system with selected communication protocols before deployment. The simulation tool identifies the system's limits in terms of delays, the number of simultaneously supported wearables, and packet loss. We identify and examine some well-known wireless connectivity protocols as possible candidates for eHealth wearable systems. The output of the analytical model is compared vis-à-vis data from a real-life proof-of-concept eHealth wearable system. The proposed approach estimates the numbers of served packets and blocked packets with approximate errors of 0.02% and 0.2%, respectively.