Adaptive Sampling Algorithms with Local Emergency Detection for Energy Saving in Wireless Body Sensor Networks

Nowadays, Wireless Body Sensor Networks (WBSN) are emerging as a low cost solution for healthcare application to find new solutions, regarding patient monitoring which is becoming the elusive requirement. Quicker emergency detection is the main purpose to create a quicker reaction and treatment if required, such as an abnormal variation of the respiration rate, which satisfies the goal of extending life expectancy. This process can help all the chronic patients who are most of the time living alone or in nursing homes. However, the limited lifetime bio-medical sensors bring on the energy consumption challenge as one of the leading challenges in WBSN. Moreover, detecting locally an emergency is also one of the main challenges in WBSN. In this paper, we propose an adaptive sampling approach, based on fisher test theory, that estimates and adapts the sensing frequency based on previous readings and the patient criticality. The main goal is to optimize the energy consumption. Furthermore, we show how emergency alerts can be supported locally on each node of the network. To validate the effectiveness of our approach we conducted several series of simulations and built a simple energy saving comparison.