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Evolutionary deployment and local search-based movements of 0th responders in disaster scenarios

机译：灾难情况下第零响应者的演化部署和基于本地搜索的移动

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The establishment of communications in disaster scenarios is of paramount importance, especially because preexisting communication infrastructure is likely to be destroyed or malfunctioning. Consequently, there is a need for an alternative and self-organizing communication infrastructure that can be rapidly deployed in disaster situations. In this paper, we propose to use drones or unmanned aerial vehicles as 0th responders to form a network that provides communication services to victims. Finding the best positions of the 0th responders is a non-trivial problem and is, therefore, divided into two phases. The first phase is the initial deployment, where the 0th responders are placed using partial information on the disaster scenario. In the second phase, which we call the adaptation to real conditions, the drones move according to a local search algorithm to find positions that provide better coverage to the victims. We conduct extensive simulations to validate our proposed approach for rural disaster scenarios under different conditions. We show that our proposed initial deployment based on genetic algorithm provides coverage for up to 94% (maximum) and 86% (mean) of victims if complete knowledge of the disaster scenario is known and 10 drones are used. When the adaptation to the real condition phase is used, this percentage is increased to 95% (maximum). If no knowledge of the scenario and 10 UAVs are used 80% (maximum) and 59% (mean) of victims are found and successfully covered. The proposed approach outperforms in 6.4% the random deployment method, and in 2.4% the best grid deployment approach. Finally, we show that by using different numbers of drones for the two phases of the proposed approach, the percentage of victims is increased up to 51% for low values of knowledge of the scenario.
机译：在灾难情况下建立通信至关重要，尤其是因为先前存在的通信基础设施很可能被破坏或发生故障。因此，需要可以在灾难情况下快速部署的替代性和自组织的通信基础结构。在本文中，我们建议使用无人机或无人驾驶飞机作为第0响应者，以形成一个为受害者提供通信服务的网络。找到第0个响应者的最佳位置是一个不容易的问题，因此分为两个阶段。第一阶段是初始部署，其中使用有关灾难场景的部分信息来放置第0个响应者。在第二阶段，我们称其为适应实际情况，无人机会根据本地搜索算法移动，以找到能够为受害者提供更好掩护的位置。我们进行了广泛的模拟，以验证我们在不同条件下针对农村灾害情况提出的方法。我们表明，如果完全了解灾难情况并使用了10架无人机，那么我们建议的基于遗传算法的初始部署可以覆盖多达94％（最大）和86％（平均）的受害者。当使用对实际状态阶段的适应时，该百分比增加到95％（最大）。如果不了解情况，则使用10架无人飞行器，发现并成功掩盖了80％（最大）和59％（平均）的受害者。所提出的方法的性能优于随机部署方法的6.4％，而最佳网格部署方法的性能则为2.4％。最后，我们表明，对于拟议方法的两个阶段，使用不同数量的无人机，由于对情景的了解不高，受害者的比例提高到51％。

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