R-IoT: An Architecture Based on Recoding RLNC for IoT Wireless Network with Erase Channel

Julio, Yair Rivera; García, Ismael; Marquez, Jose

doi:10.1007/978-3-030-40690-5_55

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…When there are three subnets with Galois fields GF(2 2 ), GF(2 4 ), and GF(2 8 ), we can observe similar performance as shown in Fig. 11 and Fig.…”

Section: Resultssupporting

confidence: 64%

“…(3) Considering the multiplication operator over finite field, when calculating the result of normal polynomial multiplication, the result of multiplication shall perform module R which is an irreducible polynomial of degree l. The operation module could be treated as replacing by R -repeatedly until all the terms with degree higher than −1 are replaced as shown in (4).…”

Section: Decoding With Multiple Field Sizes and Reduction Methodsmentioning

confidence: 99%

“…The use of linear network coding for storage in distributed clouds in order to reduce storage and traffic costs when adding and removing numerous storage devices/clouds on-the fly was proposed in [3]. In [4], a mapping of the population of packets over an RLNC coding system based on finite fields was also used to eliminate redundant information on the network for better throughput. The Reed-Solomon codes are linearized in [5] for reliability and security.…”

Section: Introductionmentioning

confidence: 99%

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Content Distribution Network for Streaming Using Multiple Galois Fields

Hung

Kaushal

Hsiao

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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In this paper, the architecture of random linear network coding based on the hybrid coding in multiple Galois field sizes is proposed. Random linear network coding is an efficient network coding approach that enables network to generate independently and randomly linear mapping between input and output symbols over finite field. With the proposed reduction method, coded symbols and coefficients in higher degree of Galois field can be converted to symbols and coefficients in GF(2) so that hybrid coding in multiple Galois field sizes can be made possible. Therefore, peers in the heterogeneous environments can all benefit from the proposed content distribution network for streaming to better utilize the network resource.

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“…When there are three subnets with Galois fields GF(2 2 ), GF(2 4 ), and GF(2 8 ), we can observe similar performance as shown in Fig. 11 and Fig.…”

Section: Resultssupporting

confidence: 64%

Section: Decoding With Multiple Field Sizes and Reduction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Content Distribution Network for Streaming Using Multiple Galois Fields

Hung

Kaushal

Hsiao

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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“…To date, sparse systematic RLNC has been mainly studied for end-to-end coding, i.e., sparse systematic RLNC encoding at the sending (source) node, and decoding at the ultimate destination node. However, an important performance-enhancing aspect of RLNC is recoding at intermediate network nodes, which can substantially increase the overall network performance [20]- [29]. With recoding, VOLUME 4, 2016 the intermediate network nodes not only store-and-forward coded packets, but rather store, recode, and forward the packets.…”

Section: A Motivationmentioning

confidence: 99%

SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

et al. 2021

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Sparse Random Linear Network Coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, which can be achieved through sending uncoded (systematic) packets. However, conventional recoding of sparse RLNC coded packets at an intermediate node in a multi-hop network increases the density of non-zero coding coefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the low density of non-zero coding coefficients of sparse RLNC with systematic packets. We develop SpaRec strategies with and without decoding at the intermediate nodes, with and without a specified coding rate, as well as with finite and infinite recoding window lengths. We evaluate the SpaRec strategies in multi-hop networks in terms of packet loss, packet delivery delay, as well as recoding and decoding (computation) throughput. We find that the SpaRec strategies substantially improve the RLNC performance compared to conventional recoding.

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