Multi-level sample importance ranking based progressive transmission strategy for time series body sensor data
2015 IEEE 16th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM) (2015)
Boston, MA, USA
June 14, 2015 to June 17, 2015
Ming Li , Department of Computer Science, California State University, Fresno, 2576 E. San Ramon, 93619, USA
Yu Cao , Department of Computer Science, University of Massachusetts Lowell, 198 Riverside St, 02148, USA
B. Prabhakaran , Department of Computer Science, The University of Texas at Dallas, 800 W. Campbell Road; MS EC31, Richardson, 75080, USA
Body sensors have gained increasing interest during the past several years. With more applications deployed, it is imperative to ensure the success of data analysis, which largely depends on data transmission reliability as well as the importance of samples received. Traditional approaches focus on improving data reliability through various schemes such as prioritization of MAC access. In this paper, we analyzed the characteristics of time series body sensor data and propose to rank sample importance based on a multi-level approach. With this approach, samples are grouped into five levels, indicating their importance with regard to data analysis. Then, a progressive transmission strategy is designed to transmit samples in order of their importance so that the overall received data quality is maximized. Preliminary simulation results indicate that as much as 40–60% bandwidth saving can be achieved while meeting the requirements of data analysis algorithms.
Reliability, Data analysis, Quality of service, Ad hoc networks, Bandwidth, Body area networks, Wireless sensor networks
M. Li, Y. Cao and B. Prabhakaran, "Multi-level sample importance ranking based progressive transmission strategy for time series body sensor data," 2015 IEEE 16th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)(WOWMOM), Boston, MA, USA, 2015, pp. 1-3.