The necessity of developing sufficient systems to monitor health conditions has increased due to the aging of the population and the prevalence of chronic diseases, creating a demand for remote health care systems that make use of biosensors. This article proposes an energy‐saving multisensor data sampling and fusion with decision‐making for the monitoring of patient health risk in wireless body sensor networks (WBSNs). The work consists of three steps: energy‐efficient sampling rate adaptation, multisensor data fusion, and decision‐making. The sampling is performed in each biosensor and it adapts its rate based on the local risk and the global risk in which global risk computed at the coordinator, where the data is fused afterward. Finally, decisions are made according to the risk level of the patient. The processing of these functions enables in real‐time the adoption of the biosensor sampling rates based on the dynamic risk level of each biosensor, and a corresponding decision is made whenever an emergency is detected. The performance of the suggested approach is evaluated using actual health datasets, and some of its aspects are put into comparison with an existing approach, such as the data reducing and energy‐consuming rates. The acquired results illustrate a decrease in the volume of gathered data, thus a significant energy saving has been made while preserving data accuracy and integrity. Moreover, presenting a data fusing model at the coordinator level by means of an early warning score system has assessed the health condition of patients and took an appropriate decision when detecting emergencies.

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SummaryOne of the best and cheapest solutions for continuous and remote monitoring and evaluation of patient health is to use the WBSNs (wireless body sensor networks) due to its great role in decreasing the expenses of the health care system. In this type of network, the sensed vital signs are gathered by biosensor devices then transmitted to the coordinator for further processing and fusion. Since the limited resources for biosensor devices (energy, memory, and processing) in addition to the periodic transmission for the large volume of data, it is essential to optimize the data transmission to save energy while keeping the data accuracy at the coordinator. This work suggests an AREDaCoT (adaptive rate energy‐saving data collecting technique) which aims to energy‐efficient patient health monitoring in periodic WBSNs. The AREDaCoT works in terms of so‐called periods. Each period has two stages: removing redundancy and adapting the sampling rate. The first stage uses improved LED to remove the redundancy in measurement of vital signs, whereas the second stage applies two techniques, assesses the risk score according to the risk level of the patient and the CSr (calibrate sampling rate) fittingly with different ranges of risk level for the sensor sampling ratio to be adapted. The performance of AREDaCoT has been evaluated in light of multiple series of simulations on real health datasets being compared to an existing approach. The acquired results illustrate how AREDaCoT decreases the volume of gathered data; thus, a significant energy saving has been made whilst preserving data accuracy and integrity. Moreover, the percentage results of data reduction over the original data set and the score differences between them are both acceptable.