Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control
This addresses the challenge of uninterrupted long-term monitoring in healthcare applications, though it appears incremental as it builds on existing fog computing concepts.
The paper tackled the problem of limited energy and computational capacity in wearable sensors for remote patient monitoring by proposing a fog-assisted control approach, which offloads tasks to higher layers and uses local closed-loop algorithms to increase battery life.
Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life.