An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

Abstract

In the Body Sensor Networks (BSNs), a comprehensive and intuitive conclusion can be obtained by analyzing the physiological state in the mode of multi-heterogeneous source fusion. However, if a node in the sensing layer fails, the input-oriented analysis system is no longer reliable. In this paper, we propose a homogenous sensor weight calculation method for a single sensing node in the BSNs. Under the premise of valid sensing, a set of sequences will be randomly selected as a referee to make the rules of weight allocation. Weak competitiveness means that sensors have a high degree of antagonism, which indicates that the measuring results have errors or there may be abnormal sensors in the sensing module. Meanwhile, we introduce the trend factor of human body state to strengthen the connection between adjacent data in time series, so as to further improve the reliability of trust weight. The proposed algorithm is helpful for sensing nodes in BSNs to obtain more reliable original physiological data and ensure the accuracy of subsequent feature analysis and system decision. Specifically, it can (1) improve the robustness of sensing in BSNs applications, (2) suppress the interference of abnormal signals, (3) solve data conflict, and (4) be universal to the sensors in BSNs devices. The ECG simulations verify the validity of the algorithm in all extreme cases of valid sensing, and the PPG experiments show that the proposed algorithm is applicable to different types of biosensors. Finally, the evaluation and comparison with other four known data-level fusion methods show that the proposed algorithm has better fusion performance.