## 5 Related Work  
[PERSON] _et al_. were the first to introduce network coding and demonstrate its usefulness [1]. After this initial work, the maximum theoretical throughput of network coding was proven, and achieved, using linear network codes, by [PERSON] and [PERSON] [39]. As suggested by [PERSON] _et al_. [27] and [PERSON] _et al_. [40], our implementations employ random linear network coding, which is believed to be the most practical approach to multicast flow scenarios, as the target to parallelize. Network coding research then spread to wireless network systems after its utility had been demonstrated by [PERSON] _et al_. [41] in that context. [PERSON] _et al_. proposed a number of practical solutions using multiple unicast flows [42] and [PERSON] _et al_. showed improvements in the reliability of ad hoc network systems [43]. The applications of network coding have been proposed in [44] and recent studies of feasibility in real testbeds have been performed and documented [45]. Especially, several previous literatures introduced to use network coding techniques in wireless sensor networks [5; 6; 7; 8; 9; 10]. [PERSON] and [PERSON] introduced a network coding based forwarding scheme for wireless sensor networks where nodes sleep most of the time [5]. [PERSON] and [PERSON] handle the problem of survivability of many-to-one flows in wireless sensor networks (WSN) using the network coding technique [6]. In addition, [PERSON] _et al_. proposed AdapCode, which is a reliable data dissemination protocol developed for any software update. Their proposed method relies on adaptive network coding to reduce broadcast traffics in the process of dissemination [9]. Using network coding in the design of practical health care wireless sensor networks is also presented in [10]. Using multi-core processors in the cloud computing environment also has been proposed [46]. In addition, [PERSON] _et al_. introduced a discussion of the utility of network coding in mobile systems [47]. Further, [PERSON] _et al_. showed that smooth, fast downloads and efficient server utilization can be achieved using network coding [4]. Lastly, [PERSON] and [PERSON] consider adoption the network coding to practical applications in mobile networks with the Apple iPhone [48]. Parallelized network coding was first suggested by [PERSON] and [PERSON] [28]. The authors used hardware acceleration and proposed a multi-threaded design utilizing multi-core systems. Research has also been conducted, from a variety of perspectives, which focuses on reducing the computational complexity of encoding/decoding operations [49; 50]. [PERSON] _et al_. suggested enhanced forms of parallelization network coding algorithms with reduced computational complexity [31; 51]. Whereas, our work is focused on improving decoding performance via the adoption of algorithms for use in a heterogeneous processor, referred to as the Cell BE. Many algorithms have been proposed to parallelize matrix calculation, such as the parallelization of matrix inversion [52], parallel LU decomposition [29], and parallelization of Gauss-Jordan elimination with block-based algorithms [30]. However, due to the network transfer delay, [PERSON] _et al_. employ a more aggressive method of network coding, referred to as _\"progressive\"_ decoding [28]. Approaches to enhancing the performance of the progressive decoding were proposed in _Parallelized Progressive Network Coding_[28]. The approaches are based on Gauss-Jordan elimination algorithm. A simple description of one variant of Gauss-Jordan elimination, as explained in [28], is presented in Table 1 of this paper. Over the entire decoding process, _Stage A_ and \(E\) comprise the majority of the workload; according to [28], _Stage A_ makes up 50.05% of the workload, while _Stage E_ has 49.5%. The load-balancing problem has been emphasized in divisible load theory [21; 22; 23]. [PERSON] and [PERSON] introduced a method of verifying divisible load size for heterogeneous distributed systems [53]. [PERSON] _et al_.