Earthquakes are one of the most destructive natural disasters harming life and the infrastructure of cities. After an earthquake, functioning communication and computational capacity are crucial for rescue teams and healthcare of victims. Therefore, an earthquake can be investigated for dynamic capacity enhancement in which additional resources are deployed since the surviving portion of the infrastructure may not meet the demand of the users. In this study, we propose a new computation paradigm, air computing, which is the air vehicle assisted next generation edge computing through different air platforms, in order to enhance the capacity of the areas affected by an earthquake. To this end, we put forward a novel paradigm that presents a dynamic, responsive, and high-resolution computation environment by explaining its corresponding components, air layers, and essential advantages. Moreover, we focus on the unmanned aerial vehicle (UAV) deployment problem and apply three different methods including the emergency method, the load balancing method, and the location selection index (LSI) method in which we take the delay requirements of applications into account. To test and compare their performance in terms of the task success rate, we developed an earthquake scenario in which three towns are affected with different severity. The experimental results showed that each method can be beneficial considering the circumstances, and goal of the rescue.

翻译：地震是破坏性最强的自然灾害之一，对生命和城市基础设施造成严重威胁。震后，稳定的通信与计算能力对救援团队和伤员救治至关重要。当幸存基础设施无法满足用户需求时，可通过部署额外资源实现动态容量增强。本研究提出一种新型计算范式——空中计算，即通过不同空中平台辅助的下一代边缘计算技术，以提升地震受灾区域的容量。为此，我们构建了包含对应组件、空中层级及核心优势的动态、高响应、高分辨率计算环境新范式。重点聚焦无人机部署问题，提出了三种方法：应急方法、负载均衡方法和考虑应用延迟需求的位置选择指数方法。为测试并比较各方法的任务成功率性能，我们构建了三个乡镇遭受不同严重程度破坏的地震场景。实验结果表明，每种方法均能根据具体情境与救援目标发挥独特优势。