KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Novel Redundant Data Storage Algorithm Based on Minimum Spanning Tree and Quasi-randomized Matrix

  • Wang, Jun (Department of Communication and Information Engineering Nanjing University of Posts and Telecommunications) ;
  • Yi, Qiong (Department of Communication and Information Engineering Nanjing University of Posts and Telecommunications) ;
  • Chen, Yunfei (School of Engineering, University of Warwick) ;
  • Wang, Yue (Department of Communication and Information Engineering Nanjing University of Posts and Telecommunications)
  • Received : 2016.10.31
  • Accepted : 2017.08.09
  • Published : 2018.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

Keywords

References

  1. Ren Fengyuan, Huang Hai and Lin Chuang, "Wireless sensor networks(In Chinese)," Journal of Software, vol. 14, no. 7, pp. 1282-1291, 2003.
  2. Akyildiz I F, Su W, Sankarasubramaniam Y, et al, "Wireless sensor networks: a survey," Computer networks, vol. 38, no. 4, pp. 393-422, 2002. https://doi.org/10.1016/S1389-1286(01)00302-4
  3. Liu Kunpeng, JiangWeidong, "The research of underwater sensor node coverage model based on perception factor (In Chinese)," Journal of Nanjing University (natural sciences), vol. 51, no. 6, pp. 1203-1209, 2015.
  4. Huang Renjie, Song Wen-Zhan, Xu Mingsen, et al, "Real-world sensor network for long-term volcano monitoring: design and findings," IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 2, pp. 321-329, 2012. https://doi.org/10.1109/TPDS.2011.170
  5. Tan Xin, Sun Zhi, Akyildiz I F, "Wireless underground sensor networks: MI-based communication systems for underground applications," IEEE Antennas and Propagation Magazine, vol. 57, no. 4, pp. 74-87, 2015. https://doi.org/10.1109/MAP.2015.2453917
  6. Cobos M, Perez-Solano J J, Felici-Castell S, et al, "Cumulative-sum-based localization of sound events in low-cost wireless acoustic sensor networks," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 12, pp. 1792-1802, 2014. https://doi.org/10.1109/TASLP.2014.2351132
  7. Liu Peng, Zhang Song, Qiu Jian, et al, "A redistribution method to conserve data in isolated energy-harvesting sensor networks," Computer Science and Information Systems, vol. 8, no. 4, pp. 1009-1025, 2011. https://doi.org/10.2298/CSIS110420066L
  8. Shen Chao, "A study of distributed fountain codes based data collection techniques in wireless sensor networks (In Chinese)," Master's diss. Hangzhou:HangzhouDianzi University, 2011.
  9. XueXinyu, Hou Xiang, Bagai Rajiv, "Data preservation in intermittently connected sensor networks with data priority," in Proc. of 10th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, pp.122-130, 2013.
  10. Shen Yulong, Xi Ning, Pei Qingqi, et al, "Distributed storage schemes for controlling data availability in wireless sensor networks," in Proc. of 7th International Conference on Computational Intelligence and Security, pp.545-549, 2011.
  11. Tang Bin, JaggiNeeraj, Takahashi Masaaki, "Achieving Data K-Availability in Intermittently Connected Sensor Networks," in Proc. of 23rd International Conference on Computer Communication and Networks, pp.1-8, 2014.
  12. Lin Yunfeng, Liang Ben, Li baochun, "Data Persistence in large-scale sensor networks with decentralized fountain codes," in Proc. of 26th IEEE International Conference on Computer Communications, pp.1658-1666, 2007.
  13. Zhang Wei, Zhang Qinchao, Xu Xianghua, Wan Jian, "An optimized degree strategy for persistent sensor network data distribution," in Proc. of 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing, pp.130-137, 2012.
  14. Aly S A, Kong Z N, Soljanin E, "Fountain codes based distributed storage algorithm for large-scale wireless sensor networks," in Proc. of International Conference on Information Processing in Sensor Networks, pp.171-182, 2008.
  15. XiaoYilong, "Random data redundancy method and it application in distributed storage systems (In Chinese)," PhD diss. Chengdu: School of Computer Science and Engineering, 2013.
  16. Camila H. S. Oliveira, Yacine Ghamri-Doudane, Carlos E. F. Brito, et al, "Optimal network coding-based in-network data storage and data retrieval for IoT/WSNs," in Proc. of 14th IEEE International Symposium on Network Computing and Applications, pp.208-215, 2015.
  17. Cheng Zhan, Fuyuan Xiao, "Coding-based storage design for continuous data collection in wireless sensor networks," in Proc. of Journal of Communications and Networks, pp.493-501, 2016.
  18. Bin Gu, Victor S. Sheng, Shuo Li, "Bi-parameter space partition for cost-sensitive SVM," in Proc. of 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), pp.3532-3539, 2015.
  19. Bin Gu, Xingming Sun, Victor S. Sheng, "Structural minimax probability machine," in Proc. of IEEE Transactions on Neural Networks and Learning Systems, pp.1-11, 2016.
  20. Zhihua Xia, Xinhui Wang, Xingming Sun, et al, "Steganalysis of least significant bit matching using multi-order differences," in Proc. of Security and Communication Networks, pp.1283-1291, 2014.
  21. Zhihua Xia, Xinhui Wang, Xingming Sun, et al, "Steganalysis of LSB matching using differences between nonadjacent pixels," in Proc. of Multimed Tools Appl, pp.1947-1962, 2016.