Minimal Increase Network Coding for Dynamic Networks

Guoyin Zhang, Xu Fan and Yanxia Wu

PLOS ONE, 2016, vol. 11, issue 2, 1-9

Abstract: Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0148725

DOI: 10.1371/journal.pone.0148725

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