**7. Conclusions**

Wearable devices are the heart of IoMT. Energy-harvesting techniques can achieve energy-autonomous wearable devices. However, handling tasks that require intensive computing resources limits their performance. To overcome these limitations, energy-aware task-offloading approaches were proposed to reduce the device energy consumption and improve computation resources. This paper surveys recent works on joint task offloading and energy-harvesting techniques in the IoMT. In addition, possibilities of power supply for medical sensors and energy-storage strategies are investigated.

Joint task offloading and energy harvesting is still an active area of research. The offloading is meaningful at two possible levels: from wearables (IoT end device) to edge devices (IoT high-end or middle-end device), or from edge devices to fog nodes. An off-policy-based reinforcement learning algorithm has been often proposed in the literature. Nevertheless, its hardware implementation has received scant attention.

Future work will focus on the efficient hardware implementation of joint energy harvesting and reinforcement learning-based task offloading for wearable devices. Nevertheless, privacy and security might affect the offloading strategy when applied to wearables; this topic was not considered in this study.

**Author Contributions:** M.B.A., I.B.D. and D.E.H. contributed equally to the manuscript concept, methodology, original draft writing, visualization and editing. S.S., A.F. and O.K. contributed by reviewing, visualization and editing. I.B.D. secured the funding of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The publication of this article was funded by Chemnitz University of Technology.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
