Open AccessMinimal Increase Network Coding for Dynamic Networks
Author(s) -
Guoyin Zhang,
Fan Xu,
Yanxia Wu
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0148725
Subject(s) - linear network coding , computer science , encoding (memory) , coding (social sciences) , network topology , dynamic network analysis , computational complexity theory , algorithm , theoretical computer science , topology (electrical circuits) , mathematics , computer network , artificial intelligence , statistics , combinatorics , network packet
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.