Adaptive Sampling Rate Determination for Energy Efficiency of Wireless Body Area Networks

Considering the facts that existing biosensors in Wireless Body Area Networks (WBANs) - that are in charge of collecting vital sign data of patients, performing preprocessing and sending them to a data fusion center - have limitations in energy consumption and data processing capability of biosensors, therefore, sampling rate has a direct impact on network energy consumption and its useful lifetime. Regarding the above issues, this paper presents an adaptive approach to determining optimal sampling rate for data management. In this regard, by using National Early Warning Score (NEWS) system, the biosensors collect relevant data and recognize the emergency information locally. To optimize nodes' activity, information about the patient's activity is extracted so that when the patient's activity is normal some of the nodes are transferred from active mode to sleep mode. Also, to determine the exact sampling rate, a statistical test was first developed to evaluate the variance associated with the patient's vital signs. Then, by using an appropriate interpolation function, the optimal sampling rate is determined. The interpolation function uses two main factors to determine the sampling rate: the patient's risk information and the values measured by the pivot node. Simulation results show about 66 percent optimization in number of communicated data and increasing more than 2.5 times of network lifetime.