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Sensors 2016, 16(9), 1385; doi:10.3390/s16091385

Adaptive Sampling-Based Information Collection for Wireless Body Area Networks

1
School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 10087, China
2
State Key Laboratory of Networking and Switch Technology, Beijing University of Posts and Telecommunications, Beijing 10087, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yun Liu, Han-Chieh Chao, Pony Chu and Wendong Xiao
Received: 30 June 2016 / Revised: 16 August 2016 / Accepted: 17 August 2016 / Published: 31 August 2016
View Full-Text   |   Download PDF [2150 KB, uploaded 31 August 2016]   |  

Abstract

To collect important health information, WBAN applications typically sense data at a high frequency. However, limited by the quality of wireless link, the uploading of sensed data has an upper frequency. To reduce upload frequency, most of the existing WBAN data collection approaches collect data with a tolerable error. These approaches can guarantee precision of the collected data, but they are not able to ensure that the upload frequency is within the upper frequency. Some traditional sampling based approaches can control upload frequency directly, however, they usually have a high loss of information. Since the core task of WBAN applications is to collect health information, this paper aims to collect optimized information under the limitation of upload frequency. The importance of sensed data is defined according to information theory for the first time. Information-aware adaptive sampling is proposed to collect uniformly distributed data. Then we propose Adaptive Sampling-based Information Collection (ASIC) which consists of two algorithms. An adaptive sampling probability algorithm is proposed to compute sampling probabilities of different sensed values. A multiple uniform sampling algorithm provides uniform samplings for values in different intervals. Experiments based on a real dataset show that the proposed approach has higher performance in terms of data coverage and information quantity. The parameter analysis shows the optimized parameter settings and the discussion shows the underlying reason of high performance in the proposed approach. View Full-Text
Keywords: wireless body area networks; data collection; information quantity; data sampling wireless body area networks; data collection; information quantity; data sampling
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Xu, X.; Zhao, F.; Wang, W.; Tian, H. Adaptive Sampling-Based Information Collection for Wireless Body Area Networks. Sensors 2016, 16, 1385.

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