KSII Transactions on Internet and Information Systems (TIIS)

  • 제12권1호
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  • Pages.227-247
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  • 2018
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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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)
  • 투고 : 2016.10.31
  • 심사 : 2017.08.09
  • 발행 : 2018.01.31
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초록

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.

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