Transmission Power Control using Vital Signs Data Prediction in Wireless Body Area Networks

ABSTRACT

Wireless Body Area Network (WBAN) is a wireless sensor network composed of sensors implanted under the skin or wearable sensors. These sensors are small and battery powered, making power efficiency an important and critical consideration. Data transmission is one of the most power consuming functions in the sensor node. This paper analyzes reducing data transmission, and hence power consumption, by predicting vital signs data instead of transmitting them all the time. We have focused on predicting the body vital signs like the temperature from other vital signs like the heart rate and the respiration rate. It is shown that the percentage of energy reduction depends on the rate of the prediction. Also, sending critical data in the alternating modes consumes more energy compared with the critical and the alternative prediction modes. It is shown that the critical alternating and critical transmission modes consumes more energy in Covid-19 patient compared to healthy person with MAE does not exceed 0.24. Finally, the multivariant model shows a great advantage in accuracy over univariant model. © 2022 IEEE.