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首页> 外文会议>International conference on mobile, secure, and programmable networking >Mobility Prediction in Vehicular Networks: An Approach Through Hybrid Neural Networks Under Uncertainty
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Mobility Prediction in Vehicular Networks: An Approach Through Hybrid Neural Networks Under Uncertainty

机译：车载网络中的移动性预测：不确定性下的混合神经网络方法

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Conventionally, the exposure regarding knowledge of the inter vehicle link duration is a significant parameter in Vehicular Networks to estimate the delay during the failure of a specific link during the transmission. However, the mobility and dynamics of the nodes is considerably higher in a smart city than on highways and thus could emerge a complex random pattern for the investigation of the link duration, referring all sorts of uncertain conditions. There axe existing link duration estimation models, which perform linear operations under linear relationships without imprecise conditions. Anticipating, the requirement to tackle the uncertain conditions in Vehicular Networks, this paper presents a hybrid neural network-driven mobility prediction model. The proposed hybrid neural network comprises a Fuzzy Constrained Boltz-mann machine (FCBM), which allows the random patterns of several vehicles in a single time stamp to be learned. The several dynamic parameters, which may make the contexts of Vehicular Networks uncertain, could be vehicle speed at the moment of prediction, the number of leading vehicles, the average speed of the leading vehicle, the distance to the subsequent intersection of traffic roadways and the number of lanes in a road segment. In this paper, a novel method of hybrid intelligence is initiated to tackle such uncertainty. Here, the Fuzzy Constrained Boltz-mann Machine (FCBM) is a stochastic graph model that can learn joint probability distribution over its visible units (say n) and hidden feature units (say m). It is evident that there must be a prime driving parameter of the holistic network, which will monitor the interconnection of weights and biases of the Vehicular Network for all these features. The highlight of this paper is that the prime driving parameter to control the learning process should be a fuzzy number, as fuzzy logic is used to represent the vague and uncertain parameters. Therefore, if uncertainty exists due to the random patterns caused by vehicle mobility, the proposed Fuzzy Constrained Boltzmann Machine could remove the noise from the data representation. Thus, the proposed model will be able to predict robustly the mobility in VANET, referring any instance of link failure under Vehicular Network paradigm.
机译：传统上，关于车辆间链路持续时间的知识的曝光是车辆网络中的重要参数，用于估计传输期间特定链路故障期间的延迟。但是，在智能城市中，节点的移动性和动态性要比在高速公路上高得多，因此，在参考各种不确定条件的情况下，对于链接持续时间的调查可能会出现复杂的随机模式。存在现有的链路持续时间估计模型，其在线性关系下执行线性运算而没有不精确的条件。预期解决车辆网络中不确定条件的需求，本文提出了一种混合神经网络驱动的移动性预测模型。所提出的混合神经网络包括模糊约束玻尔兹曼机器（FCBM），该模型允许了解单个时间戳中若干车辆的随机模式。可能使车辆网络的上下文不确定的几个动态参数可能是预测时的车速，领先车辆的数量，领先车辆的平均速度，到随后的交通道路交叉口的距离以及路段中的车道数量。在本文中，提出了一种新的混合智能方法来解决这种不确定性。在这里，模糊约束玻尔兹曼机（FCBM）是一种随机图模型，可以学习其可见单位（例如n）和隐藏特征单位（例如m）的联合概率分布。显然，必须有一个整体网络的主要驱动参数，它将监控所有这些特征的车辆网络的权重和偏差的互连。本文的重点是控制学习过程的主要驱动参数应为模糊数，因为模糊逻辑用于表示模糊和不确定的参数。因此，如果由于车辆移动性引起的随机模式而存在不确定性，则所提出的模糊约束玻尔兹曼机可以消除数据表示中的噪声。因此，所提出的模型将能够参考车辆网络范式下的链路故障的任何实例，来稳健地预测VANET中的移动性。

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