Earticle

초록

영어

Wireless Sensor Networks (WSNs) have characteristics of large size, limited resources, large amount of transmission data, and so on. In order to reduce the redundancy of sensed data and decrease network data traffic. We applied CS to clustered structure, proposed Low-Latency Compressed Sensing model (LLCS) which is based on the spatial-temporal correlation of sensed data, the model is also capable of processing sparse abnormal events which is a crucial feature in WSNs. We analyzed the relationship between compression ratio and sampling rounds and verified the abnormal event processing method. The results of simulation experiments using the real data show that LLCS could reduce data transfer volume significantly and process abnormal readings effectively.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Low Latency Compressed Sensing Model
  3.1. Compressed Sensing
  3.2. Model Building
  3.3. Compression and Reconstruction
  3.4. Recover Signal with Sparse Abnormal Readings
 4. Performance Evaluation
 5. Conclusions and Further Work
 Acknowledgments
 References

키워드

저자

• Jun Wang [ Dept. of Computer & Software, Nanjing University of Information Science & Technology, Nanjing China, Dept. of Information Construction & Management, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Shuqiang Ji [ Dept. of Computer & Software, Nanjing University of Information Science & Technology, Nanjing China ]
• Yong Cheng [ Dept. of Information Construction & Management, Nanjing University of Information Science & Technology, Nanjing 210044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605