ecoSync: An Energy-Efficient Clock Discipline Data Synchronization in Wi-Fi IoMT Systems

Abstract

The growth of the Internet of Medical Things (IoMT) and healthcare data analytics allows wearable Wireless Body Area Networks (WBANs) and ambient sensors to collect the large quantities of physiological signals necessary for better patient diagnostics and treatments. Artificial intelligence and machine learning algorithms frequently require precisely synchronized signals from multiple sensors, which in turn require time-consuming and energy-inefficient synchronization methods with constant wireless network connectivity. We propose ecoSync, a highly energy-efficient time synchronization algorithm for Wi-Fi devices in IoMT applications. We demonstrated that ecoSync can correct the time difference error to ±42 µs with an hour between resynchronizations, using only 658 millijoules of energy. This is an 87% improvement in time difference error and a 99.93% reduction in energy usage over using TSF for synchronization alone over a 1 h period. Wireless synchronization of sensors allows placement of physiological sensors on objects of everyday use (Smart Stuff), which in turn allows seamless collection of physiological status data every time we interact with smart objects in an IoMT environment.