Runtime Adaptive IoMT Node on Multi-Core Processor Platform

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, state-of-the-art portable devices, IoTM opens up new scenarios for data collection continuous patient monitoring. Two very important aspects should be considered make the most this paradigm. For first aspect, moving processing task from cloud edge leads several advantages, such as responsiveness, portability, scalability, reliability sensor node. second order increase accuracy system, cognitive algorithms based on artificial intelligence deep learning must integrated. Sensory nodes often need battery powered remain active a long time without different power source. Therefore, one challenges addressed during design development IoMT devices concerns energy optimization. Our work proposes an implementation analysis techniques resource-constrained computing platform. To handle efficiency, we introduced component called Adaptive runtime Manager (ADAM). This takes care reconfiguring hardware software device dynamically execution, better adapt it workload required operating mode. test high computational load multi-core Orlando prototype board by STMicroelectronics, Electrocardiogram (ECG) traces have been adopted, considering single-channel six-channel simultaneous cases. Experimental results show that managing sensory node configuration at runtime, savings least 15% can achieved.