Deep-learning based forecasting sampling frequency of biosensors in wireless body area networks

Abstract

Wireless Body Area Networks (WBANs) have been introduced as a useful way in controlling health status of the monitored patients, during recent years. Each WBAN includes a number of biosensors attached to the patient’s body, collecting his vital sign features and communicating them to the coordinator to make appropriate decisions. Managing energy consumption of biosensors and continuous monitoring of the patients are two main issues in WBANs. Hence, denoting efficient sampling frequency of biosensors is very important in WBANs. In this paper, we propose a scheme which aims at determining and forecasting sampling rate of active biosensors in WBANs. In this regard, from the first round until a certain round, the sampling rate of biosensors would be determined. Accordingly, we introduce our modified Fisher test, develop spline interpolation method and introduce three main parameters. These parameters are information of patient’s activity, patient’s risk and pivot biosensor’s value. Then, by employing mentioned parameters in addition to the introduced statistical and mathematical based strategies, the sampling rate of active biosensors in the next round would be determined at the end of each entire round. By reaching a pre-denoted round, the sampling rate of biosensors would be predicted through forecasting methods. For this purpose, we develop two machine learning based techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS) and Long Short Term Memory (LSTM). For estimation our approaches we simulate them in MATLAB R2018b software. Simulation results demonstrate that our methods can decrease the number of communicated data by 81%, reduce energy expenditure of biosensors by 73% and forecast the sampling rate of biosensors in the future rounds with 97% accuracy and 2.2753 RMSE.