Cost Minimization for Cooperative Traffic Relaying Between Primary and Secondary Networks
Cost Minimization for Cooperative Traffic Relaying Between Primary and Secondary Networks
复制标题
主次网络之间协作流量中继的成本最小化
DOI：
10.1109/tmc.2018.2795607
复制
发表时间：
2018
影响因子：
7.9
通讯作者：
Yang Zhen
中科院分区：
文献类型：
--
作者：
Tian Feng;Yuan Xu;Hou Y Thomas;Lou Wenjing;Yang Zhen
主网络和辅助网络之间的合作在数据转发方面提供了显著的益处。但是，这种合作所涉费用问题并不十分清楚。在本文中，我们探讨了成本在主网络和辅助网络时，他们被允许以合作的方式中继对方的流量。我们在这两个网络的成本模型，并制定一个多目标优化问题。对于这个问题，我们提出了一种新的算法来构建一个<inline-formula><tex-math notation="LaTeX">$\displaystyle $</tex-math><alternatives><inline-graphic xlink:href="yuan-ieq1-2795607.gif"/></alternatives></inline-formula>-近似曲线，并证明其误差界的两个成本维度。基于<inline-formula><tex-math notation="LaTeX">$\displaystyle $</tex-math><alternatives><inline-graphic xlink:href="yuan-ieq2-2795607.gif"/></alternatives></inline-formula>-逼近曲线，我们开发了三个重要的应用。第一个应用是显示单个目标的最小成本值（通过将另一个目标固定为常数）或在整个可能值范围内两个目标之间的关系。第二个应用是解决主网络和辅网络中的不同成本参数。我们展示了如何获得一个新的近似曲线，通过缩放原始<inline-formula><tex-math notation="LaTeX">的近似</tex-math><alternatives><inline-graphic xlink:href="yuan-ieq3-2795607.gif"/></alternatives></inline-formula>曲线与适当的因素，并量化其误差界。第三个应用是使用<inline-formula><tex-math notation="LaTeX">$\displaystyle $</tex-math><alternatives><inline-graphic xlink:href="yuan-ieq4-2795607.gif"/></alternatives></inline-formula>-逼近曲线来研究具有保证误差界的单目标优化问题。本文中的结果提供了一些深刻的理论见解，在两个网络中所产生的潜在成本时，他们被允许中继对方的流量合作。
Cooperation between primary and secondary networks offers significant benefits in data forwarding. But, cost implication in such cooperation is not well understood. In this paper, we explore cost incurred in both primary and secondary networks when they are allowed to relay each other's traffic in a cooperative manner. We model costs in both networks and formulate a multiobjective optimization problem. For this problem, we present a novel algorithm to construct an <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives> <inline-graphic xlink:href="yuan-ieq1-2795607.gif"/></alternatives></inline-formula>-approximation curve and prove its error bounds in both cost dimensions. Based on the <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math> <alternatives><inline-graphic xlink:href="yuan-ieq2-2795607.gif"/></alternatives></inline-formula>-approximation curve, we develop three important applications. The first application is to show the minimum cost value for a single objective (by fixing the other objective as constant) or the relationship between the two objectives over the entire range of possible values. The second application is to address different cost parameters in the primary and secondary networks. We show how to obtain a new approximation curve by scaling the original <inline-formula> <tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives><inline-graphic xlink:href="yuan-ieq3-2795607.gif"/> </alternatives></inline-formula>-approximation curve with appropriate factors and quantify its error bounds. The third application is to use the <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives> <inline-graphic xlink:href="yuan-ieq4-2795607.gif"/></alternatives></inline-formula>-approximation curve to study a single objective optimization problem with a guaranteed error bound. The results in this paper offer some deep theoretical insights on potential costs incurred in both networks when they are allowed to relay each other's traffic cooperatively.