Baris Yamansavascilar

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.