2022-03-04 23:17:15

Computers and Mathematics with Applications 64 (2012) 3869–3880

Contents lists available atSciVerse ScienceDirect

Computers and Mathematics with Applications

journal homepage:www.elsevier.com/locate/camwa

Large scale simulation for human evacuation and rescue

Erol Gelenbelowast;, Fang-Jing Wu 1

Intelligent Systems and Networks Group, Department of Electrical amp; Electronic Engineering, Imperial College, London SW7 2AZ, UK

a r t i c l e i n f o


Cyber-physical systems Emergency management Search and rescue systems Distributed decision making Modeling and simulation

a b s t r a c t

This paper surveys recent research on the use of sensor networks, communications and computer systems to enhance the human outcome of emergency situations. Areas covered include sensing, communication with evacuees and emergency personnel, path finding algorithms for safe evacuation, simulation and prediction, and decision tools. The systems being considered are a special instance of real-time cyber-physical-human systems that have become a crucial component of all large scale physical infrastructures such as buildings, campuses, sports and entertainment venues, and transportation hubs.

copy; 2012 Elsevier Ltd. All rights reserved.

Introduction and overall vision

Cyber-technical systems (CPS) that exploit wireless technologies, micro-sensing micro-electro-mechanical-systems (MEMS), and distributed decision making and control, have enriched the confluence of ubiquitous computing, networking technologies, and wireless sensor networks (WSNs), boosting many promising applications in environmental sensing [1], health monitoring [2], surveillance [3], intelligent transportation [4], guiding groups on tourist tours [5], and emergency response [6,7]. In particular this paper focuses on sensor-aided CPSs that enable intelligent and fast response to emergencies such as fires, earthquakes, or terrorist attacks. Real-time monitoring and quick response are inherent requirements in the design of an emergency response system. As an example, during a fire many different types of sensors can cooperate to interact with civilians and the environment. Temperature and gas sensors are responsible for monitoring the spreading of hazards. Rotatable cameras track the spread of the fire and the movement of civilians. Ultrasonic sensors can range the distance to obstacles in the environment, and monitor dynamic changes of maps due to the sudden changes of some built structures through destruction and the accumulation of debris. Intelligent evacuation scheduling can be conducted by the cooperation between first-aid decision nodes, sensors, and civilians with mobile devices since partial information and opportunistic connection are usually inevitable in an emergency. Civilians with mobile devices will follow personalized navigation directions and distributed decisions may help mitigate congestion, while those without mobile devices may follow audio or visible LED directions from nodes in their neighborhood. Grid/Cloud-supported simulators will gather all sensing information to dynamically predict and forecast the spread of hazards and to make decisions on resource allocation and response policies.

    1. Approaches to emergency response

Two types of approaches to emergency response, Approach 1 and Approach 2 ofFig. 1, have motivated considerable research. One approach addresses the evacuation of victims of an emergency with the aid of fixed wireless sensor networks, so that the evacuation process responds dynamically to the manner in which hazards spread or recede. In this approach, work has also been devoted to directing first responders and emergency personnel towards the events that are taking place

lowast; Corresponding author.

E-mail addresses: e.gelenbe@imperial.ac.uk(E. Gelenbe),f.wu@ntu.edu.sg(F.-J. Wu).

1 Present address: Nanyang Technological University, Singapore.

0898-1221/$ – see front matter copy; 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.camwa.2012.03.056

Fig. 1. Solutions to emergency disaster responses.

Fig. 2. Analysis of emergency evacuation systems.

and to aid the victims. Approach 2 focuses on the use of mobile devices, as well as sensors, so that the victims of an emergency and the emergency personnel can act autonomously based on the advice and information that they receive. Approach 3 on the other hand which we have called lsquo;lsquo;next generation systemsrsquo;rsquo; would include some of the methods and technologies that we will discuss in Section5.

Emergency evacuation

A representation of the evolution of emergency evacuation systems, from the simpler to the more complex, is given in Fig. 2. Below, we review both types of systems and discuss issues including communications, information acquisition and dissemination, knowledge discovery, heterogeneous system integration and asynchronous control.

Finding safe evacuation paths and providing them in a timely fashion to the evacuees


Computers and Mathematics with Applications 64 (2012) 3869–3880

Contents lists available atSciVerse ScienceDirect

Computers and Mathematics with Applications

journal homepage:www.elsevier.com/locate/camwa





a r t i c l e i n f o公司

关键词:网络物理系统 应急管理 搜救系统 分布式决策 建模与仿真

a b s t r a c t公司



1. 介绍与总体设想

利用无线技术,微传感微机电系统(MEMS)以及分布式决策和控制的网络技术系统(CPS)丰富了无处不在的计算,网络技术和无线传感器网络(WSN)的融合),在环境传感 [1],健康监测 [2],监视 [3],智能交通 [4],旅游指南 [5]和应急响应 [6][7]。本文特别关注传感器辅助的CPS,这些传感器可对火灾,地震或恐怖袭击等紧急情况做出智能,快速的响应。实时监视和快速响应是应急系统设计中的固有要求。例如,在发生火灾时,许多不同类型的传感器可以协作以与平民和环境互动。温度和气体传感器负责监控危害的传播。可旋转的摄像机跟踪火势的蔓延和平民的行动。超声波传感器可以测距环境中障碍物的距离,并监视由于某些建筑结构因破坏和碎片堆积而突然变化而引起的地图动态变化。智能疏散调度可以通过急救决策节点,传感器和平民与移动设备之间的协作来进行,因为在紧急情况下通常不可避免地需要部分信息和机会连接。配备移动设备的平民将遵循个性化的导航方向,而分布式决策可能有助于缓解拥堵,而没有配备移动设备的平民则可能遵循来自其附近节点的音频或可见LED方向。网格/云支持的模拟器将收集所有感测信息,以动态预测和预测危害的扩散,并就资源分配和响应策略做出决策。配备移动设备的平民将遵循个性化的导航方向,而分布式决策可能有助于缓解拥堵,而没有配备移动设备的平民则可能遵循来自其附近节点的音频或可见LED方向。网格/云支持的模拟器将收集所有感测信息,以动态预测和预测危害的扩散,并就资源分配和响应策略做出决策。配备移动设备的平民将遵循个性化的导航方向,而分布式决策可能有助于缓解拥堵,而没有配备移动设备的平民则可能遵循来自其附近节点的音频或可见LED方向。网格/云支持的模拟器将收集所有感测信息,以动态预测和预测危害的扩散,并就资源分配和响应策略做出决策。

1.1 。应急方法

两种应急方法,图1的方法1和方法2,激发了相当多的研究。一种方法是借助固定的无线传感器网络解决紧急情况受害者的疏散问题,从而使疏散过程对危险扩散或消退的方式做出动态响应。在这种方法中,还致力于指导急救人员和紧急人员进行正在发生的事件并援助受害者。方法2集中于移动设备以及传感器的使用,以便紧急情况的受害者和紧急情况人员可以根据他们收到的建议和信息自主采取行动。另一方面,我们称为方法3的“下一代系统”将包括我们将在第5节中讨论的一些方法和技术 。


2 。紧急疏散




2.1 。潜在维护方法

[8]中,采用WSN来监视环境中的危害,并且仅假设一个出口。每个用户都配备有传感器节点,用于与WSN通信,以请求到出口的紧急疏散路径。假设每个用户都知道WSN的部署。检测危险的传感器被建模为多个障碍物。因此,目标是找到从每个传感器到出口的“最安全”路径,而不会穿过任何障碍物。人造势场的概念已在任务计划中长期使用 [9][10]采用分布式计算疏散路径。出口传感器产生吸引电势以将传感器拉到出口,而每个障碍物产生排斥势以将传感器推离障碍物。以这种方式,每个传感器可以计算用于指导撤离人员的总电势值。这里传感器的整体电位值s一世 由 P(s一世)=sum;sHisin;H1个d2(s一世,sH),在哪里 H 是检测危险的一组传感器, d(s一世,sH) 是距离的最短跳数 s一世 遇到障碍 sH (即危险传感器)。

然而,因为只有最短路径使用没有的概念危险区域, [8]ensp;,所使用的路径可以非常接近危险的来源。而且,无线链路不提供准确的导航链路,从而可能提供穿过物理障碍物(例如,墙壁)的不切实际的路径。因此,通过考虑几个危险区域,每个危险区域由一组传感器形成,这些传感器的距离危险的跳距不大于预定义的阈值d和[11]中的手动导航图, 移动自组织网络中的多路径路由概念将人们导航到尽可能远离危险区域的地方。每个传感器节点将维持一个海拔高度,以将人们引导到导航图中海拔最低的相邻传感器节点。要绕过危险区域,危险区域中的传感器节点必须通过以下方式提高其高度。当传感器s一世 由危险传感器通知 sH 与 d(s一世,sH)le;d,它将认为自己处于危险区域内,并通过 一种′(s一世)=最大值{一种(s一世),一种Euml;米Gtimes;1个d2(s一世,sH) d(s一世,sEuml;)},在哪里 一种Euml;米G是用于检测危险和 d(s一世,sEuml;) 是距离的最短跳数 s一世到出口。这里,一种′(s一世) 和 一种(s一世)用于区分更新前后的海拔高度。以来s一世 可能在多个危险区域内,这些危险区域导致的最大海拔 s一世的高度。通过设置海拔高度,某些传感器可能会成为局部最小传感器。执行部分链路反向操作以解决此问题,以便每个传感器都至少维护一个输出链路。参考 [12][11ensp;]扩展 到3D环境,其中传感器分为普通传感器,出口传感器和楼梯传感器。如果传感器位于危险区域内,则认为自己处于危险区域d远离危险,或者它是楼梯传感器且其楼下传感器位于危险区域。导航原理是在没有通往“楼下”的安全路径的情况下将人们引导到屋顶。

但是,当危害动态扩展或缩小时,应避免频繁的全局消息泛洪。因此, [13]利用局部地理路线来规划导航路径,以适应动态危险,在危险区域发生变化的情况下,只有那些没有传出链路的传感器才需要执行本地链路反向操作。假定每个传感器都知道其地理坐标。不仅要考虑危险区域,还要考虑安全区域。危险区域定义为危险度超过预定义阈值的区域,而安全区域定义为那些危险区域之外的区域。目标是为安全区域中的每个传感器找到至少一条安全路径,并至少找到一条危险区域中每个传感器的逃生路径。每个传感器s一世 在安全区域内将保持安全 ([R一世,d一世,s一世),在哪里 [R一世 是...的逆转计数器 s一世 在安全区域,指示何时 s一世 成为局部最小值 d一世是到最近出口的欧几里得距离。相反,每个传感器s一世 在危险区域保持危险媒介 ([R一世macr;,d一世macr;,s一世),在哪里 [R一世macr; 是...的逆转计数器 s一世在危险区域指示时间 s一世 成为局部最大值 d一世macr;是到危险源的欧几里得距离。基于安全矢量或危险矢量,每个传感器可以设置导航链接,以向出口移动或从危险区域逃逸。如果存在局部最小值/最大值s一世,则仅来自其反向计数器小于的相邻传感器的传入链接 s一世将被颠倒,使得每个传感器节点具有至少一个用于导航的输出链接。



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