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https://doi.org/10.1371/journal.pone.0156756
Title: | Advanced algorithms for local routing strategy on complex networks | Authors: | Lin B. Chen B. Gao Y. Tse C.K. Dong C. Miao L. Wang B. |
Keywords: | avoidance behavior algorithm computer network human software statistical model statistics and numerical data Algorithms Computer Communication Networks Humans Models, Statistical Software |
Issue Date: | 2016 | Citation: | Lin B., Chen B., Gao Y., Tse C.K., Dong C., Miao L., Wang B. (2016). Advanced algorithms for local routing strategy on complex networks. PLoS ONE 11 (7) : e0156756. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0156756 | Rights: | Attribution 4.0 International | Abstract: | Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 'T' decreases by 70-90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. © 2016 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | Source Title: | PLoS ONE | URI: | https://scholarbank.nus.edu.sg/handle/10635/161565 | ISSN: | 19326203 | DOI: | 10.1371/journal.pone.0156756 | Rights: | Attribution 4.0 International |
Appears in Collections: | Staff Publications Elements |
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