An Efficient DMO Task Scheduling Technique for Wearable Biomedical Devices

The popularity of wearable devices has grown as they improve the quality of life in many applications. In particular, for medical devices, energy harvesters are the dominating source of energy for wearable devices. However, their power budget is limited. Thus, power-saving techniques are essential components in the whole technology stack of those devices. That is, choosing the optimal schedule for different tasks running on the wearable device can help to reduce energy consumption. This paper presents a sensor task scheduling technique for optimizing energy consumption for energy harvesting-based wearable biomedical devices. The proposed technique is based on Dwarf Mongoose optimization (DMO) to compute Hamming-based Tikhonov regularization. The proposed DMO-based scheduling algorithm is assessed against the state-of-the-art technique and compares the results with our prior study’s FPA-based algorithm. The data used in the experiments are collected from an in-lab prototype.