Lengthening Battery Life Expectancy of Sensors in WBANs: a Multifactorial Approach

Abstract

[eng] Wireless Body Area Networks (WBANs) are emerging as a key component in healthcare within the Internet of Things (IoT) ecosystem, with sensor battery life being crucial for widespread adoption. To that end, the present work analyzes the different components of sensor's energy consumption with the objective of deriving analytical expressions for the battery lifespan for an ETSI SmartBAN compliant network. Results have revealed that the sensing energy consumption, commonly considered negligible, cannot always be ignored. Moreover, stringent Quality of Service (QoS) requirements of the physiological sensed data, such as error rate and end-to-end delay, must be fulfilled. Therefore, our approach synergizes an energy-efficient and QoS-aware PHY/MAC configuration framework with sensor energy harvesting. To further increase the WBANs autonomy, the present proposal integrates adaptive sampling mechanisms at sensors. Additionally, this research incorporates the patient's status information and the sensor's battery level to regulate the behavior of the system. This novel multifactorial approach has allowed an in-depth and comprehensive investigation of the synergies and mutual influences among the different components that integrate this multipronged proposal, demonstrating a significant potential for lengthening the sensor's battery life expectancy and to substantially extend the WBANs autonomy. Notably, adaptive sampling markedly improves battery lifespan, especially with higher harvestable power levels and shorter MAC frames. In scenarios with lower battery charge or improved patient conditions, the adaptive sampling framework notably enhances system performance and battery lifetime. An 'average' case study, considering a medium patient critical level and a 50% battery charge, shows that adaptive sampling can increase battery duration up to 840.94%, significantly boosting WBANs autonomy.