Abstract
The growing number of IoT devices have brought up the opportunity for large data utilization and the challenge of energy availability due to the large number of devices that need energy to function reliably. One such source of energy is battery, especially for mobile IoT devices. Battery technology has lagged behind IoT device miniaturization in terms of size and weight, rendering them unsuitable or extremely challenging to replace for the majority of IoT device applications. A method for enhancing energy availability in mobile devices’ batteries or getting rid of them entirely is radio frequency energy harvesting. These make Deep Learning attractive for resource-constrained devices such as Energy Harvesting Cognitive Internet of Things (EH CIoT) devices. This work discusses Deep Learning schemes for EH CIoT devices paying particular attention to the energy efficiency, speed of execution and complexity of the deployed models.
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