In this paper, we propose a scheme which aims at determining and forecasting sampling rate of active biosensors in Wireless Body Area Networks (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, introduce three main parameters namely information of patient's activity, patient's risk and pivot biosensor's value. Then, by employing these parameters plus introduced statistical and mathematical based strategies, the sampling rate of the active biosensors in the next round would be determined at the end of each entire round. After reaching a pre-denoted round the sampling rate of biosensors would be predicted through forecasting methods. In this regard, we develop two machine learning based techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS) and Long Short Term Memory (LSTM) and compare them with four famous similar techniques. In addition to using forecasted sampling frequencies of the biosensors for controlling their energy expenditure, these forecasted values would also be used to forecast patient's status in the future. This is the first work in this domain that uses current information of the patient to determine adaptive sampling frequency and then employs the time series of determined sampling frequencies to forecast the patient's status and biosensors energy expenditure in the future. For estimating our schemes, we simulated them in MATLAB R2018b software and compared the results with a number of similar schemes. Based on the simulation results, the proposed schemes are capable to reduce data traffic by 81%, decrease energy consumption of the network by 73% while having the capability of predicting sampling rate of biosensors with 97% accuracy.

在本文中，我们提出了一种旨在确定和预测无线人体局域网（WBAN）中有源生物传感器采样率的方案。在这方面，从第一轮到某一轮，将确定生物传感器的采样率。因此，我们介绍了改进的Fisher检验，开发了样条插值法，介绍了三个主要参数，即患者活动信息，患者风险和关键生物传感器的价值。然后，通过使用这些参数以及引入的基于统计和数学的策略，可以在每个完整周期的末尾确定下一轮中的有源生物传感器的采样率。在到达预定的回合后，将通过预测方法来预测生物传感器的采样率。在这方面，我们开发了两种基于机器学习的技术，即自适应神经模糊推理系统（ANFIS）和长期短期记忆（LSTM），并将它们与四种著名的相似技术进行了比较。除了使用生物传感器的预测采样频率来控制其能量消耗外，这些预测值还将用于将来预测患者的状况。这是该领域的第一项工作，它使用患者的当前信息来确定自适应采样频率，然后使用确定的采样频率的时间序列来预测患者的状况和将来的生物传感器能量消耗。为了评估我们的方案，我们在MATLAB R2018b软件中对它们进行了仿真，并将结果与​​许多类似方案进行了比较。根据模拟结果，