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Avoiding high impacts of geospatial events in mission critical and emergency networks using linear and swarm optimization

机译:使用线性和群体优化避免在关键任务和紧急网络中的地理空间事件对地理空间事件的高影响力

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Geospatial events continue to plague both wireless and wireline communication networks. The immediate effect of a large scale geospatial event is generally complete or partial loss of situational awareness caused by a lack of communications and media availability in the affected area, effectively isolating many people affected. Significant challenges have been reported by emergency responders, victims, and other involved persons immediately following the recent U.S. tornados and the earthquakes worldwide. Riots and protests can also act as geospatial events that stress communications resources in a geographic area. This research develops novel optimization models to identify and mitigate geospatial vulnerabilities in network designs before they occur. We use an integer linear program (ILP) to add nodes that reduces geographic vulnerability thus preventing users from being isolated by geospatial events. To expand the scope to include more solutions and a wider range of objective functions, a swarm optimization approach was also developed. Wireless propagation models that include obstructions like buildings and other terrain features are tested with these models as well.
机译:地理空间事件继续扰乱无线和有线通信网络。大规模地理空间事件的直接影响通常是由于受影响地区缺乏通信和媒体可用性而导致的情境意识的完全或部分丧失,从而有效地隔离了许多受影响的人。紧急响应者,受害者和其他涉及的人在最近的美国龙卷风和全球地震之后,应当有重大挑战。骚乱和抗议也可以作为地理空间事件,在地理区域中压力通信资源。本研究开发了新颖的优化模型,以在发生之前识别和减轻网络设计中的地理空间漏洞。我们使用整数线性程序(ILP)来添加节点,从而降低地理漏洞,从而阻止用户由地理空间事件隔离。为了扩展范围,包括更多解决方案和更广泛的客观功能,还开发了一种群体优化方法。包括建筑物和其他地形特征等障碍物的无线传播模型也与这些模型进行了测试。

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