Article: Standard deviation (SD)-based data filtering technique for body sensor network data Journal: International Journal of Data Science (IJDS) 2015 Vol.1 No.2 pp.189 - 203 Abstract: It has been observed that sometimes non-repeated, but redundant values are transmitted unnecessarily between body sensor network (BSN) and database server (DBS). The redundant value increases overhead without giving any conclusion. We have proposed a Standard Deviation (SD) based data filtering technique which improves the PDA performance by compacting the sensed data. We have introduced three categories (normal, critically below normal, and critically above normal) of the patient which are bound by predetermined boundaries. This span of boundary is known as 'Window Size'. This research work is restricted to controlling the size of windows so that the amount of sensed data being transmitted can be increased to utilise maximum network bandwidth. This window size variability is presented as 'elasticity'. The ultimate objective of collected data is to be analysed to identify patient's state so that medical expert may take appropriate actions accordingly. The proposed SD-based data filtering technique increase the size of window dynamically to accommodate more data without compromising the tone. Our experiments demonstrate that SD-based BSN data filtering can reduce the data up to 40%. The proposed SD-based technique is compared with 3-Sigma rule to show their applicability. Inderscience Publishers - linking academia, business and industry through research

Title: Standard deviation (SD)-based data filtering technique for body sensor network data

Authors: Basant Tiwari; Abhay Kumar

Addresses: F-91/59, Tulsi Nagar, Opp. Jawahar Bal Bhawan, Bhopal – 462003 (M.P.), India ' School of Electronics Devi Ahilya University Takshashila Campus, Khandwa Road, Indore – 452 001 (M.P.), India

Abstract: It has been observed that sometimes non-repeated, but redundant values are transmitted unnecessarily between body sensor network (BSN) and database server (DBS). The redundant value increases overhead without giving any conclusion. We have proposed a Standard Deviation (SD) based data filtering technique which improves the PDA performance by compacting the sensed data. We have introduced three categories (normal, critically below normal, and critically above normal) of the patient which are bound by predetermined boundaries. This span of boundary is known as 'Window Size'. This research work is restricted to controlling the size of windows so that the amount of sensed data being transmitted can be increased to utilise maximum network bandwidth. This window size variability is presented as 'elasticity'. The ultimate objective of collected data is to be analysed to identify patient's state so that medical expert may take appropriate actions accordingly. The proposed SD-based data filtering technique increase the size of window dynamically to accommodate more data without compromising the tone. Our experiments demonstrate that SD-based BSN data filtering can reduce the data up to 40%. The proposed SD-based technique is compared with 3-Sigma rule to show their applicability.

Keywords: standard deviation; BSNs; body sensor networks; physiological values; data filtering; data reduction; database servers; network bandwidth.

DOI: 10.1504/IJDS.2015.072420

International Journal of Data Science, 2015 Vol.1 No.2, pp.189 - 203

Received: 30 Dec 2013
Accepted: 12 Jun 2014
Published online: 13 Oct 2015 *

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Title: Standard deviation (SD)-based data filtering technique for body sensor network data

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