The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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An Estimation Model of Missing Data for Smart Phone Sensing

스마트폰 센싱을 위한 손실 데이터 추정 모델

  • 민홍 (호서대학교, 모바일시스템공학) ;
  • 허준영 (한성대학교, 컴퓨터공학과)
  • Received : 2013.04.08
  • Accepted : 2013.06.14
  • Published : 2013.06.30
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Abstract

Smart phones that are equipped with various types of sensors can monitor human beings, and their social activities and environments. Smart phone sensing systems are inevitable to lose sensing data at a certain region. It is more severe effect on opportunistic sensing because this sensing scheme is designed to read values from sensors when the state of numberous users meets pre-defined conditions. In this paper, we suggested an estimation model of missing data considering features of smart phone sensing to solve lower quality of collected data. This proposed model does not only reflect a temporal and spatial correlation, but also give high priority to participants who provide high quality data to improve the accuracy of estimated values. The experimental results show that our scheme is more accurate than previous scheme.

스마트폰에 탑재된 다양한 종류의 센서들을 활용하여 사용자의 상태나 사회활동 및 주변 환경을 모니터링하는 스마트폰 센싱 시스템에서 특정 지역의 데이터가 손실되는 문제는 피할 수 없다. 다수의 사용자를 대상으로 사전에 정의해 놓은 조건이 만족할 때 센서로부터 측정된 값을 서버로 전송하는 기회기반 센싱 기법에서는 이러한 데이터 손실 문제가 더 심화된다. 본 논문에서는 수집된 데이터의 품질 저하 문제를 해결하기 위해 스마트폰 센싱의 특성을 고려한 손실 데이터 추정 모델을 제안한다. 제안된 추정 모델에서는 데이터의 시공간적 상관관계를 고려할 뿐만 아니라 신뢰도가 높은 데이터를 제공하는 참여자의 우선순위를 높임으로써 향상된 추정 값을 도출하도록 설계하였다. 또한 실험결과를 통해 본 논문에서 제안한 기법이 기존의 기법들에 비해 높은 신뢰도를 보이는 것을 알 수 있었다.

Keywords

References

  1. A. T. Campbell, "From Smart to Cognitive Phones," IEEE Pervasive Computing, Vol. 11, No.3, pp.7-11, Jun. 2012. https://doi.org/10.1109/MPRV.2012.41
  2. W. Z. Khan, Y. Xiang, M. Y. Aalsalem, Q. Arshad, "Mobile Phone Sensing Systems: A Survey," IEEE Communications Surveys & Tutorials, Vol.15, No.1, pp.402-427, Apr. 2013. https://doi.org/10.1109/SURV.2012.031412.00077
  3. Y. Kang, and M. Hong, "Sensory Data Processing by Using Hadoop Framework," Journal of Korean Institute of Information Technology, Vol. 11, No. 2, pp. 169-174, Feb. 2012.
  4. R. Gant, F. Ye, H. Lei, "Mobile Crowdsensing: Current State and Future Challenges," IEEE Communications Magazine, Vol. 49, No. 11, pp.32-39, Nov. 2011.
  5. J. Kim, "A Layer-based Dynamic Unequal Clustering Method in Large Scale Wireless Sensor Networks," Journal of the Korea Academia- Industrial cooperation Society, Vol. 13, No. 12, pp.6081-6088, Dec. 2012. https://doi.org/10.5762/KAIS.2012.13.12.6081
  6. T. Das, P. Mohan, V. N. Padmanabhan, R. Ramjee, A. Sharma, "PRISM: platform for remote sensing using smartphones," The 8th international conference on Mobile systems, applications, and services, pp.63-76, Jun. 2010.
  7. N, Lane, S. Eisenman, M. Musolesi, E. Miluzzo, A. Campbell, "Urban Sensing Systems: Opportunistic or Participatory?" The 9th Workshop on Mobile Computing Systems and Applications, pp. 11-16, Feb. 2008.
  8. L. Pan, J. Li, "K-Nearest Neighbor Based Missing Data Estimation Algorithm in Wireless Sensor Networks," Wireless Sensor Network, Vol. 2, No. 2, pp. 115-122, Feb. 2010. https://doi.org/10.4236/wsn.2010.22016
  9. S. Reddy, D. Estrin, M. Srivastava, "Recruitment Framework for Participatory Sensing Data Collections," The 8th International Conference on Pervasive Computing, pp. 138-155, May 2010.
  10. O. Ghica, G. Trajcevski, P. Scheuermann, Z. Bischof, N. Valtchanov, "SIDnet-SWANS: a simulator and integrated development platform for sensor networks applications, The 6th ACM conference on Embedded networked sensor systems," pp. 385-386, Nov. 2008.
  11. C. Kim, N. Kim, "Collision Avoidance Power Control of Carrier Sensing Zone for Energy Efficiency in Wireless Sensor Network," The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 11, No. 4, pp. 53-60, Aug. 2011