Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 294591, 8 pages.

Big Data Reduction and Optimization in Sensor Monitoring Network

Bin He and Yonggang Li

Full-text: Open access

Abstract

Wireless sensor networks (WSNs) are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.

Article information

Source
J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 294591, 8 pages.

Dates
First available in Project Euclid: 27 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425050741

Digital Object Identifier
doi:10.1155/2014/294591

Citation

He, Bin; Li, Yonggang. Big Data Reduction and Optimization in Sensor Monitoring Network. J. Appl. Math. 2014, Special Issue (2014), Article ID 294591, 8 pages. doi:10.1155/2014/294591. https://projecteuclid.org/euclid.jam/1425050741


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