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Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks
Author(s) -
Shawqi Jaber Alaa,
Kadhum Idrees Ali
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4589
Subject(s) - computer science , wireless sensor network , redundancy (engineering) , real time computing , vital signs , wireless , energy (signal processing) , transmission (telecommunications) , volume (thermodynamics) , data redundancy , data mining , telecommunications , computer network , statistics , medicine , database , mathematics , physics , surgery , quantum mechanics , operating system
Summary One 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.