## 1 Introduction  
Network coding is a new coding technique first proposed by [PERSON] _et al_. to enhance network throughput and effectiveness on multi-nodal environments [1] such as wireless sensor networks (WSN). A new paradigm has emerged for computer network systems enabled by network coding; advances in network coding techniques have influenced information and coding theory, computer network performance, and wired/wireless communication systems. In addition, network coding lends itself particularly well to multicasting, enhancing the effectiveness of multicasting compared to traditional coding approaches. In fact, use of network coding techniques to various real world applications has been introduced [2, 3]. Further more, network coding has the potential to deliver a number of benefits in various domains such as wireless networks, sensor networks, network security, peer-to-peer (P2P), and on-demand video streaming service [2, 4, 5, 6, 16]. In wireless network systems, the network coding can increase transmission efficiency at routers by forwarding the coded packets as a passive acknowledgement [11] and can increase performance of ad-hoc networks as well as save the energy with many to many broadcast environment [12, 13]. In addition, the coded packets which are on the fly cannot be decoded until the sufficient number of packets are collected, thus network coding can simplify implementation of secure network as well [14, 15]. In practical approach, [PERSON] _et al_. analyze the performance of network coding on real-world commercial systems with 200 GBytes of real-world traces which had been collected during Summer Olympic Games in 2008 [16]. While network coding has several advantages and is a promising technique for the future of network systems, one crucial drawback is the associated volume of computational overhead, which may hinder its adoption in practical use. Network coding requires encoding the data before it is sent and decoding it after it is received. However, the decoding algorithm has \(O(n^{3})\) computational complexity, using a variant of Gaussian elimination where \(n\) is the size of a coefficient vector. The computation overhead associated with the decoding operation is very costly, especially with the low computing environment such as wireless sensor networks. As a result, the benefits of the network coding technique may be canceled out by the long decoding delay. On the other hand, multi-core processors have recently become widespread and can be found in a variety of systems [17], from high performance servers to special purpose wireless sensor networks [18, 19]. In fact, the current research on sensor networks mainly uses a light-weighted processing node as a sensor node. However, we also expect that the future WSN systems would require more computing power, especially for the multimedia sensors. Therefore, the multi-core processor would be a possible choice for the sensor node. Especially, a prototype of multi-core platform as a sensor node is introduced in the previous research [18]. This paper is based on the expectation that the future WSN would popularly use multi-core processors and require parallelized random linear network coding. In addition, using advanced microprocessor features such as the multimedia extension in WSN is investigated in the previous literature as well [20]. In fact, processor development has resulted in a progressively increasing number of cores in a single chip. There are two kinds of multi-core processor design paradigm; one group integrates homogeneous multiple cores on a single chip whereas the other group incorporates heterogenous cores. In this paper, we present a parallel algorithm of network coding for heterogeneous multi-core processors especially targeting to utilize the technique in WSN. We select the already available heterogeneous platform, the Cell BE, as a prototype of heterogeneous multi-core processors and adjust the workload distribution on each core for efficient network coding.