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Data envelopment analysis with slacks model for energy efficient multicast over coded packet wireless networks

机译:具有松弛模型的数据包络分析,用于通过编码的分组无线网络进行节能多播

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摘要

Green communication has recorded much attention in industry, academia and government. It has been recorded that information communication and technology nodes consume roughly 3% of the world-wide energy supply and are responsible for 2% of the global carbon dioxide (CO) emission. As a result, researchers have devoted huge attention to prevent its growth. In order to address this problem we studied coded packet approach, which is the current method of minimising energy in wireless multicast networks. Then, we propose novel approaches that are based on data envelopment analysis (DEA) to further optimise energy consumption in wireless multicast networks. We found that existing approaches to energy efficient multicast are unsuitable for evaluating efficiency adequately. This paper takes the position that true efficiency evaluation is obtained when both inputs and outputs with multiple variables are considered in measuring performance using ratios of weighted outputs to weighted inputs. As a result, we developed the input-oriented variable return to scale (VRS) envelopment with slacks models for energy efficiency in wireless multicast networks. We explored the random linear network coding (RLNC) based on simulation approach and compared the results with the input-oriented VRS DEA envelopment with slacks approach. The results show the DEA approach substantially saves energy compared to the RLNC. Furthermore, we show that DEA method has the capability to identify which network is inefficient and projected them onto the efficient frontier.
机译:绿色通信已在工业界,学术界和政府中引起了广泛关注。据记录,信息通信和技术节点消耗了全球约3%的能源供应,占全球二氧化碳(CO)排放量的2%。结果,研究人员投入了巨大的精力来防止其生长。为了解决这个问题,我们研究了编码分组方法,这是在无线多播网络中使能量最小化的当前方法。然后,我们提出了一种基于数据包络分析(DEA)的新颖方法来进一步优化无线多播网络中的能耗。我们发现,现有的节能多播方法不适合充分评估效率。本文认为,在使用加权输出与加权输入之比来衡量绩效时,如果同时考虑具有多个变量的输入和输出,便可以获得真正的效率评估。结果,我们开发了带有松弛模型的面向输入的可变规模收益(VRS)信封,以提高无线组播网络的能源效率。我们基于仿真方法探索了随机线性网络编码(RLNC),并将结果与​​采用松弛法的面向输入的VRS DEA包络进行了比较。结果表明,与RLNC相比,DEA方法大大节省了能源。此外,我们证明DEA方法具有识别哪个网络效率低下并将其投影到有效边界上的能力。

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