Data preservation in intermittently connected sensor networks with data priority

Xue, Xinyu; Hou, Xiang; Tang, Bin; Bagai, Rajiv

doi:10.1109/sahcn.2013.6644970

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Therefore, how to design an effective data storage algorithm to preserve sensory data in intermittently connected sensor networks has become a hot research area in recent years. This paper focuses on the data storage problem of intermittently connected wireless sensor networks [9] (ICWSNs) deployed in hash environments. Such networks are usually unattended, which means that sink nodes may appear periodically to collect sensory data, or fixed sink nodes are vulnerable to natural disasters and artificial attacks.…”

Section: Introductionmentioning

confidence: 99%

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

2018

KSII TIIS

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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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Section: Introductionmentioning

confidence: 99%