Fast Parallel Implementation for Random Network Coding on Embedded Sensor Nodes

Choi, Seong Min; Lee, Kyogu; Park, Joon‐Sang

doi:10.1155/2014/974836

Cited by 9 publications

(9 citation statements)

References 19 publications

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“…We note for completeness that a custom very large scale integration (VLSI) design for network coding has been studied in [51]. Computational strategies for GF (2 8 ) network coding on general-purpose multicore CPUs have mainly focused on judiciously partitioning the coefficient and data matrices to facilitate parallel processing [52]- [55]. These partitioning strategies have greatly sped up the network coding computations (and reduced the energy consumption [56], [57]).…”

Section: Related Work On Network Coding Computationsmentioning

confidence: 99%

“…Some additional speed up can be achieved by scheduling the matrix block operations according to the dependency structure of the computations in a DAG [21]. A key drawback of the DAG approach in [21] is that it is limited to non-progressive decoding of a full generation of coded packets; whereas, most of the partitioning approaches [52]- [55] are suitable for progressive RLNC decoding. The present study seeks to bring the benefits of DAG scheduling to progressive RLNC decoding.…”

Section: Related Work On Network Coding Computationsmentioning

confidence: 99%

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Progressive Multicore RLNC Decoding With Online DAG Scheduling

2019

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A complete generation of packets coded with Random Linear Network Coding (RLNC) can be quickly decoded on a multicore system by scheduling the involved matrix block operations in parallel with an offline (pre-recorded) directed acyclic graph (DAG). The waiting for a complete generation of packets can be avoided with progressive RLNC decoding that commences the decoding (and can decode some packets) before all packets in a generation have been received. This article develops and evaluates a novel progressive RLNC decoding strategy based on the principle of DAG scheduling of parallel matrix block operations. The novel strategy involves helper matrices for conducting the Gauss Jordan elimination based on rows of blocks of matrix elements. The matrix block computations are dynamically scheduled by an online DAG which permits branching, e.g., to skip unnecessary matrix block operations. The throughput and delay of the novel progressive RLNC decoding strategy are evaluated with experiments on two heterogeneous multicore processor boards. The novel progressive RLNC decoding achieves throughput levels on par with stateof-the-art non-progressive (full-generation) RLNC decoding and achieves three times higher throughput than the fastest (highest-throughput) known progressive RLNC decoder for small generation sizes and short data packets. Also, our progressive RLNC decoding greatly reduces receiver delays for moderate to large generation sizes; the delay reductions are particularly pronounced when a low-delay RLNC version is employed (e.g., reduction to one tenth of the non-progressive decoding delay for a generation size of 256 packets). INDEX TERMS Directed acyclic graph (DAG), helper matrix, heterogeneous multicore architecture, online scheduling, parallel computing, random linear network coding (RLNC).

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Section: Related Work On Network Coding Computationsmentioning

confidence: 99%

Section: Related Work On Network Coding Computationsmentioning

confidence: 99%

Progressive Multicore RLNC Decoding With Online DAG Scheduling

2019

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“…A number of works have considered ways to improve the throughput and reduce the decoding latency of RNC [3][4][5][6][7][8][9][10][11][12][13]. RNC was proposed by Ho et al [1] as a capacity-achieving scheme for multicasting in wired networks following the work by Ahlswede et al [2] who first suggested network coding and showed its utility in wireline networks.…”

Section: Related Workmentioning

confidence: 99%

“…Otherwise, users of the system cannot experience the benefit of RNC. To overcome the computational overhead of RNC, a number study focused on the performance of RNC [3][4][5][6][7][8][9][10][11][12][13]; recent advances have succeeded in mitigating the concerns. According to one recent study [13], the throughput of an optimized RNC decoder on smartphone platforms is over 1 Gbps, which well exceeds H.264 1080p30 FHD bandwidth requirement of 30 Mbps.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient QoS-Aware Random Network Coding on Smartphones

Shin

Park

2017

Mobile Netw Appl

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Random network coding (RNC) technology is known to benefit various facets of information networks; however, there have been concerns for the computational complexity of RNC since its incipience. For instance, RNC's high complexity can be directly translated into high energy consumption and drain fast smartphone batteries, making it unsuitable for mobile environments. In this paper, we optimize the energy consumption of RNC implementations with a given QoS requirement, especially throughput, for smartphone environments. To this end, we propose a duty cycling approach minimizing the energy consumption of RNC with a given throughput constraint. By manipulating the processor clock frequency controlling mechanism (a.k.a. governor) in Android, our approach can indirectly regulate the processor clock frequency and enhance energy efficiency. Real experiments on Android systems with smartphone application processors such as Samsung's Exynos 5410, show that our method can reduce the energy consumption of RNC by up to 67% compared to a RNC implementation relying on ondemand governor for frequency control. Finally, we argue that our method can be applied to a wide range of applications by implementing it with a fast Fourier transform algorithm.

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“…Associated applications were gathered, and their needs merged into three so-called requirement profiles as part of the HiFlecs project [24]. Corresponding needs were gathered from all ZDKI projects and scientifically evaluated by an associated research study [25].…”

Section: Introductionmentioning

confidence: 99%

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

Ramly

Nordin

Abdullah

2022

Wireless Communications and Mobile Computing

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The future communication requirements for industrial automation will differ significantly from existing technologies. Traffic in Industry 4.0 imposes real-time requirements, requiring ultra-reliable communication (URC) with high reliability and minimal latency. The demand for ultra-high reliability as high as 99.999999 percent and as low as 1 ms end-to-end latency is the major challenge of the NOMA communication system in the factory of the future. The high expectations on reliability and latency need modifications to the radio system’s baseband signal processing, medium access control (MAC) layer, and application layer to protect against packet losses. Thus, this paper investigates the utilization of Raptor Q codes, which is a type of Application Layer Forward Error Correction (AL-FEC), by developing an end-to-end system level simulator to evaluate and analyze the performance of the transmission signals based on cross-layer approach; from the physical layer (PHY), MAC layer, and application layer parameters in an indoor factory network setting. The factory is assumed to be operated with various factory robots of different speeds, from static to 10 km/h. The 5G technology relies heavily on flexible network operations. High user density, high user mobility, deployment, and coverage are all qualities that allow for this flexibility. Through extensive simulations, the results showed that the Raptor Q codes are not only able to give good results, i.e., packet reception rate PRR = 0.9 of 10 m or 1.8% to 10 m or 3.4% depending on different scenarios, but are also able to meet PRR = 0.9 in the mobility scenario at 10 km/h. Thus, the Raptor Q codes can be seen as a good candidate for obtaining results within a strict range of requirements set by URC communications for the factory of the future replacing RLNC.

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Fast Parallel Implementation for Random Network Coding on Embedded Sensor Nodes

Cited by 9 publications

References 19 publications

Progressive Multicore RLNC Decoding With Online DAG Scheduling

Progressive Multicore RLNC Decoding With Online DAG Scheduling

Energy Efficient QoS-Aware Random Network Coding on Smartphones

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

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