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A battery level aware MADM combination for the vertical handover decision making

机译：用于垂直切换决策的电池电量感知MADM组合

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The internet services became so various, that the number of its users is exponentially increased these last two decades, and wireless technologies are developed as well. Nowadays, people are willing to use mobile devices to connect wirelessly anywhere, anytime and to any available technology under the ABC (Always Best Connected) concept. To access the bet service wirelessly, mobile nodes “MNs” try to connect to the best wireless network technology available anytime, even when quitting the base station or access point area to one another, or changing the wireless access type, which is called vertical handover (VH). This supposes that the MN must connect to the wireless network available, that offers the best quality of service (QoS), without session breaks. To reach this objective, VH decision-making need to be quick and efficient to determine which wireless technology is the best, among those available anytime. Thus, we are seeking to optimize the network selection phase in the process of VH. Several network selection algorithms have been used, based on different theories, such as basic algorithms (based on simple metrics), Neural Networks models, Gaming theory algorithms and Multi Attributes Decision Making (MADM) methods. This paper presents a comparison of different MADM methods, taking into account an important criterion which is the battery level, so as to determine which method makes the best VH decisions, while extending the MN's battery life.
机译：互联网服务变得如此多样化，以至于在过去的二十年中，其用户数量呈指数增长，并且无线技术也得到了发展。如今，人们愿意使用移动设备随时随地以无线方式连接到ABC（始终最佳连接）概念下的任何可用技术。为了无线访问投注服务，移动节点“ MN”会尝试随时连接到可用的最佳无线网络技术，即使在彼此退出基站或接入点区域或更改无线访问类型（称为垂直切换）时也是如此。 （VH）。这假定MN必须连接到可用的无线网络，该无线网络提供最佳的服务质量（QoS），而不会中断会话。为了实现此目标，VH决策需要快速有效地确定哪种无线技术是最佳的，这是随时可用的。因此，我们正在寻求在VH过程中优化网络选择阶段。基于不同的理论，已经使用了多种网络选择算法，例如基本算法（基于简单度量），神经网络模型，游戏理论算法和多属性决策（MADM）方法。本文介绍了不同的MADM方法的比较，并考虑了一个重要的标准，即电池电量，以确定哪种方法可以做出最佳的VH决策，同时延长MN的电池寿命。

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