Fast Parallel Implementation for Random Network Coding on Embedded Sensor Nodes

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Network coding is becoming essential part of network systems since it enhances system performance in various ways. To take full advantage of network coding, however, it is vital to guarantee low latency in the decoding process and thus parallelization of random network coding has drawn broad attention from the network coding community. In this paper, we investigate the problem of parallelizing random network coding for embedded sensor systems with multicore processors. Recently, general purpose graphics processing unit (GPGPU) technology has paved the way for parallelizing random network coding; however, it is not an option on embedded sensor nodes without GPUs and thus it is indispensable to leverage multicore processors which are becoming more common in embedded sensor nodes. We propose a novel random network coding parallelization technique that can fully exploit multicore processors. In our experiments, our parallel method exhibits over 150% throughput enhancement compared to existing state-of-the-art implementations on an embedded system.