Big Data Reduction and Optimization in Sensor Monitoring Network | Zendy

Bin He | Zendy; Yonggang Li | Zendy

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Big Data Reduction and Optimization in Sensor Monitoring Network

Author(s) -

Bin He,

Yonggang Li

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/294591

Subject(s) - wireless sensor network , computer science , volume (thermodynamics) , reduction (mathematics) , real time computing , data compression , big data , data transmission , data mining , transmission (telecommunications) , computer data storage , computer network , algorithm , telecommunications , computer hardware , physics , geometry , mathematics , quantum mechanics

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

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