Electrical & Computer Engineering, Department of


First Advisor

Yi Qian

Date of this Version



Jose Matamoros, Yi Qian, Aerial Base Station Deployment for Post-Disaster Public Safety Applications, Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, 2019


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Telecommunications Engineering, Under the Supervision of Professor Yi Qian. Lincoln, Nebraska: April, 2019.

Copyright (c) 2019 Jose Antonio Matamoros Vargas


Earthquakes and floods are constant threats to most of the countries in the world. After such catastrophes, a rapid response is needed, which includes communications not only for first responders but also for local civilians. Even though there are technologies and specialized personnel for rapid deployment, it is common that external factors will hinder the arrival of help while communication requirements are urgently required. Such communication technologies would aid tasks regarding organization and information dissemination from authorities to the civilians and vice-versa. This necessity is due to protocols and applications to allocate the number of emergency resources per location and to locate missing people.

In this thesis, we investigate the deployment problem of Mobile Aerial Base Stations (MABS). Our main objective is to ensure periodic wireless communication for geographically spread User Equipment (UE) based on LTE technology.

First, we establish a precedent of emergency situations where MABS would be useful. We also provide an introduction to the study and work conducted in this thesis.

Second, we provide a literature review of existing solutions was made to determine the advantages and disadvantages of certain technologies regarding the described necessity.

Third, we determine how MABS, such as gliders or light tactical balloons that are assumed to be moving at an average speed of 50 km/h, will be deployed. These MABS would visit different cluster centroids determined by an Affinity Propagation Clustering algorithm. Additionally, a combination of graph theory and Genetic Algorithm (GA) is implemented through mutators and fitness functions to obtain best flyable paths through an evolution pool of 100.

Additionally, Poisson, Normal, and Uniform distributions are utilized to determine the amount of Base Stations and UEs. Then, for every distribution combination, a set of simulations is conducted to obtain the best flyable paths. Serviced UE performance indicators of algorithm efficiency are analyzed to determine whether the applied algorithm is effective in providing a solution to the presented problem.

Finally, in Chapter 5, we conclude our work by supporting that the proposed model would suffice the needs of mobile users given the proposed emergency scenario.

Adviser: Yi Qian