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Reliable data transmission in wireless sensor networks with data decomposition and ensemble recovery

College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, P.R.China

Special Issues: Internet of Things (IoT)-Based Environmental Intelligence

Wireless sensor networks (WSNs) are usually used to helps many basic scientific works to gather and observe environmental data, whose completeness and accuracy are the key to ensuring the success of scientific works. However, due to a lot of noise, collision and unreliable data link, data loss and damage in WSNs are rather common. Although some existing works, e.g. interpolation methods or prediction methods, can recover original data to some extent, they maybe provide an unsatisfac-tory accuracy when the missing data becomes large. To address this problem, this paper proposes a new reliable data transmission scheme in WSNs by using data decomposition and ensemble recovery mechanism. Firstly, the original data are collected by sensor nodes and then are expanded and split into multiple data shares by using multi-ary Vandermonde matrix. Subsequently, these data shares are transmitted respectively to source node via the sensor networks, which is made up of a large number of sensor nodes. Since each share contains data redundancy, the source node can reconstruct the original data even if some data shares are damaged or lost during delivery. Finally, extensive simulation experi-ments show that the proposed scheme outperforms significantly existing solutions in terms of recovery accuracy and robustness.
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References

1. H. Shen, X. Li, Q. Cheng, et al., Missing information reconstruction of remote sensing data: A technical review, IEEE Geosc. Rem. Sen. M., 3 (2015), 61–85.

2. M. Chen, S. Mao and Y. Liu, Big data: A survey, Mobile Netw. Appl., 19 (2014), 171–209.

3. A. Sandryhaila and J. Moura, Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure, IEEE Signal Proc. Mag., 31 (2014), 80–90.

4. T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE T. Inform. Theory, 13 (1967), 21–27.

5. L. Kong, D. Jiang and M. Wu, Optimizing the spatio-temporal distribution of cyber-physical systems for environment abstraction, 2010 IEEE 30th International Conference on Distributed Computing Systems (ICDCS), Genoa, Italy, June 21–25, (2010), 179–188.

6. H. Zhu, Y. Zhu, M. Li, et al., SEER: Metropolitan-scale traffic perception based on lossy sensory data, in Proceedings of IEEE International Conference on Computer Communications (INFO-COM), Rio de Janeiro, Brazil, April 19-25, (2009), 217–225.

7. E. Candes, J. Romberg and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE T. Inform. Theory, 52 (2006), 489–509.

8. D. Donoho, Compressed sensing, IEEE T. Inform. Theory, 52 (2006), 1289–1306.

9. L. Kong, M. Xia, X. Liu, et al., Data loss and reconstruction in wireless sensor networks, IEEE T. Parall. Distr., 25 (2014), 2818–2828.

10. Z. Chen, L. Chen, G. Hu, et al., Data reconstruction in wireless sensor networks from incomplete and erroneous observations, IEEE Access, 6 (2018), 45493–45503.

11. F. Li, K. Wu, X. Zhang, et al., Robust batch steganography in social networks with non-uniform payload and data decomposition, IEEE Access, 6 (2018), 29912–29925.

12. X. Zhang, Matrix analysis and applications, Beijing, Tsinghua University Press, (2004), 161–166.

13. A. Sekey, A computer simulation study of real-zero interpolation, IEEE T. Audio Electroacous-tics, 18 (1970), 43–54.

14. N. Dodgson, Quadratic interpolation for image resampling, IEEE T. Image Process., 6 (1997), 1322–1326.

15. M. Wen, K. Lu, J. Lei, et al., BDO-SD: An efficient scheme for big data outsourcing with secure deduplication, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Hong Kong, China, April 26-May 1, (2015), 214–219.

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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