SBSN: Harvesting Stable Body Sensor Node by Providing an Energy Efficient Adaptive Sampling Method

Wireless Body Area Networks (WBANs) are an efficient solution to monitor the vital signs of patients. In WBAN, the sensors' operation stops as soon as the battery is discharged. Hence, it is expected that sensors can continue their operations stably to provide stable services. The problem of sensors' stability can solves by using energy harvesters and providing unlimited energy, but the main challenge is that the energy harvested at different times has different rates. Therefore, energy-saving methods should be taken into consideration along with energy-harvesting techniques. The sampling operation has the highest energy consumption in WBAN. Adaptive sampling methods conserve energy to a high degree. This paper proposes a new method for combining energy harvesting technique and adaptive sampling to create a stable body sensor node (SBSN). In SBSN, sensors are classified into three classes based on their energy levels. The rate of sampling in each class is determined according to various methods considering energy levels to ensure the self-stability of the sensors. The simulations show that, compared to the state-of-the-art methods, the SBSN can reduce the data overhead by 73% on average while conserving data integrity. In addition, SBSN makes the sensor self-stable and keeps the energy level of the sensors up to 2.9 times higher on average.