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首页> 外文期刊>Journal of Engineering & Applied Sciences >Early Alarm for Emergency Response Based on the Priority Associated with the Cooperative Awareness Messages in Vehicular Ad-hoc Network
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Early Alarm for Emergency Response Based on the Priority Associated with the Cooperative Awareness Messages in Vehicular Ad-hoc Network

机译：基于与车辆ad-hoc网络中的合作意识消息相关的优先级，提前报警

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摘要

Vehicular Ad hoc Network (VANET) is a promising technology for future smart vehicles systems and an essential component of Intelligent Emergency System (IES). The IES includes a wide range of modern technologies such as Global Positioning System (GPS), digital maps, video cameras, sensing devices and the wireless communication devices. It provides necessary information about the condition of the roads in time for drivers and traffic management systems to improve traffic efficiency, reduce traffic congestion, waiting times and fuel consumptions. Design and implement an IES which automatically controls the encryption of the Cooperative Awareness Messages (CAMs) according to the priority associated with CAMs exchanged between emergency vehicles and Road Side Units (RSUs). The CAMs sent from the emergency vehicles to RSUs be signing using a Secure Hash Algorithm (SHA-2) to distinguish them from normal messages issued from other vehicles. The IES uses the features extracted from the trace file that describes the normal and urgent behavior in the VANETs. The type and the number of features have an important role in increasing the classification accuracy rate and decreasing false alarms, especially False Negative Rate (FNR). In this study, the process of classification of urgent records by using (self-organizing map, feed-forward neural network and Elman neural network). The proposed system is based on a program written by MATLAB R2015a. Our selection used for design and programming the proposed system. These algorithms have already been employed to solve the problem because of its importance in saving time and effort as well as providing high results accuracy in quick time unlike other programming languages. The result is clear in overall system in each technique in SOM accuracy degree 99.5% and FNR 0% while FFNN accuracy degree 99.3% and FNR 0.84211% for number of features 16.
机译：车辆临时网络（VANET）是未来智能车辆系统的有希望的技术和智能紧急系统（IES）的重要组成部分。 IES包括各种现代技术，如全球定位系统（GPS），数字地图，摄像机，传感设备和无线通信设备。它提供有关驾驶员和交通管理系统的时间条件的必要信息，以提高流量效率，减少交通拥堵，等待时间和燃料消耗。设计和实现根据与在紧急车辆和路侧单元（RSU）之间交换的凸轮相关联的优先级，自动控制协作意识消息（CAM）的加密。从紧急车辆发送到RSU的凸轮使用安全散列算法（SHA-2）签名，以将它们与其他车辆发出的正常信息区分开来。 IES使用从跟踪文件中提取的功能，该功能描述了vanets中的正常和紧急行为。功能的类型和数量在增加分类精度率和减少误报时具有重要作用，尤其是假负率（FNR）。在本研究中，通过使用（自组织地图，前馈神经网络和ELMAN神经网络）来分类紧急记录的过程。所提出的系统基于Matlab R2015A编写的程序。我们的选择用于设计和编程所提出的系统。这些算法已经被用来解决问题，因为它在节省时间和精力方面的重要性以及在快速时间内提供高分辨率的准确性，与其他编程语言相比。结果在每个技术中的总系统中清晰，SOM精度为99.5％和FNR 0％，而FFNN精度为99.3％，FNR 0.84211％的特性16。

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