1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result;

2-D deployment of aerial base stations: A simulation model to provide voice communication.

2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s);

Gabriela Rodríguez-Cortés
Universidad Politécnica de Pachuca, Carretera Pachuca-Cd. Sahagún km 20, Zempoala, 43830, Hidalgo, Mexico.
glrc@micorreo.upp.edu.mx
+52 771 547 7510

Anabel Martínez-Vargas
Universidad Politécnica de Pachuca, Carretera Pachuca-Cd. Sahagún km 20, Zempoala, 43830, Hidalgo, Mexico. 
anabel.martinez@upp.edu.mx
+52 771 547 7510

MA Cosío-León
Universidad Politécnica de Pachuca, Carretera Pachuca-Cd. Sahagún km 20, Zempoala, 43830, Hidalgo, Mexico.
ma.cosio.leon@upp.edu.mx 
+52 771 547 7510

Daniela M. Martínez
Facultad de Ciencias de la Ingeniería y la Tecnología, Universidad Autónoma de Baja California, Blvd. Universitario 1000, Tijuana, 21500, Baja California, Mexico.
daniela.martinez@uabc.edu.mx
+52 664 979 7591 

Oscar Montiel
Instituto Politécnico Nacional - CITEDI, Ave. Instituto Politécnico Nacional 1310, Tijuana, 22435, Baja California, Mexico.
oross@ipn.mx
+52 664 623 1366

3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); 

Anabel Martínez-Vargas

4. the abstract of the paper(s);

Unmanned aerial vehicles (UAVs) offer a potential alternative for providing voice services in areas where communication is disrupted due to natural disasters. These UAVs can be configured as aerial base stations (ABSs), enabling the deployment of a temporary communications network. However, communication networks based on ABSs pose several significant challenges. One of these challenges involves addressing interruptions or limitations in network coverage caused by natural disasters. In such situations, there is a high likelihood that users within the affected area may be unable to communicate due to a lack of coverage. This is a complex problem because it depends on factors, such as the mobile user locations, the characteristics of
the air-to-ground channel, and geographical details of the area. In this work, we propose an optimization model to determine the placement of a set of ABSs within a limited disaster area that maximizes the probability of successful voice services (PSVSs). This optimization model integrates a network evaluation model that analyzes the wireless environment at a specific time. The network evaluation model utilizes two-ray and Rayleigh channel models, enabling
the simulation of a worst-case scenario for wireless communication systems. We evaluate the proposed optimization model using the (1+1)-evolution strategy with a one-fifth success rule. We explore various parameter configurations to understand their impact on algorithm performance. This analysis helps identify the configuration of the optimization model that yields the maximum PSVSs. Simulation results indicate that by appropriately configuring the evolution strategy algorithm and comparing random ABS locations with those determined ABS locations by the evolution strategy algorithm, the PSVS can be enhanced by an average of 60%.

5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies;

(A) The result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention.
(D) The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created.
(G) The result solves a problem of indisputable difficulty in its field.

6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission);

(A) The result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention.

The patent registration process in Mexico was initiated on December 16, 2024. The case file number is MX/a/2024/015697, and the application ID is 194151.
The inventors are Anabel Martínez Vargas, María de los Ángeles Cosío León, and Gabriela Lizeth Rodríguez Cortés.
The invention relates to a method for establishing a temporary wireless voice communication network using unmanned aerial vehicles as aerial base stations (ABSs). The method strategically positions ABSs in locations that maximize the number of voice services in areas where communication is entirely disrupted.
The method involves deploying multiple ABSs, forming a single-hop wireless network that serves as the backhaul. These ABSs must provide voice services to wireless devices in the area of interest. Additionally, a control station facilitates communication with ABSs, and at least one of these vehicles is assigned the task of determining the positions of wireless devices attempting to access the communication network.

(D) The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created.

In the article "2-D Deployment of Aerial Base Stations: A Simulation Model to Provide Voice Communication", the results obtained are significant and valuable in their scientific merit, even though they were obtained from a computer-aided scenario by means of computational simulation.
The proposed optimization model, which employs the (1+1)-evolution strategy with a one-fifth success rule, demonstrates that the strategic placement of ABSs enhances the probability of successful voice services (PSVS) in areas where communication is disrupted due to natural disasters. This finding holds significant relevance not only due to its practical applicability in emergency scenarios but also because of its substantial contributions to the advancement of methodology and the application of modeling and simulation in wireless networks. Furthermore, it enriches the theoretical understanding of modeling and simulation within the context of methods and algorithms, as well as the emerging domain of edge artificial intelligence.
The fact that the results were obtained through a simulation model does not diminish their scientific validity. On the contrary, the use of computational tools allows for the exploration of complex scenarios in a controlled and reproducible manner, reinforcing the robustness of the conclusions. In this regard, we generate an instance (see https://figshare.com/articles/dataset/RC_CMU-PCP-19819_GZ/24194529) that utilizes real geographic coordinates for mobile users and the boundaries of the affected disaster zone. These geographic coordinates are sourced from Tula Town in Mexico, where in September 2021, the Tula River overflowed, resulting in the loss of voice and data services across 18 towns.
Furthermore, the approach proposed in the article prioritizes the provision of voice services, which are essential in emergencies, over more complex services such as data or video transmission. This prioritization is based on the fact that voice services require less bandwidth and are more accessible to users with limited devices, making them a more robust option in scenarios where terrestrial infrastructure has been damaged. This practical and efficient approach strengthens the relevance of the results, regardless of the method used to generate them.
The results presented in this article are publishable based on their scientific merit, as they contribute to advancing knowledge in the field of wireless communication networks and have practical applications in real-world scenarios. The fact that these results were obtained through computational simulation does not undermine their validity; on the contrary, it highlights the effectiveness of computational tools in addressing complex problems.

(G) The result solves a problem of indisputable difficulty in its field.

The problem addressed in this article, which involves optimizing the placement of ABSs to provide voice services in areas affected by natural disasters, is of indisputable difficulty in the field of telecommunications and emergency management.
As highlighted in [1], the effective deployment of wireless networks based on ABSs presents several challenges. Among the primary concerns in such deployments are the optimal placement of ABSs, resource allocation, interference mitigation, and accurate channel modeling. The positioning of ABSs constitutes one of the most significant challenges, as it is contingent upon dynamic factors such as mobile user distribution, air-to-ground channel properties, geographical terrain specifics, and the energy limitations inherent to ABSs operations. Determining the optimal placement of ABSs to maximize network performance is classified as NP-hard [2]. This implies that finding an optimal solution requires advanced optimization approaches, such as the model proposed in this article, which uses the (1+1)-evolution strategy to generate a good solution. The model can handle a large number of mobile users (up to 19,819 in this study) distributed across a vast area (12 km x 12 km), increasing the difficulty of ensuring effective coverage and adequate quality of service (QoS).
Furthermore, ABSs positioning directly influences critical communication network metrics, including the signal-to-interference-plus-noise ratio (SINR). This metric is itself dependent on variables such as transmitter and receiver locations, transmission power, and radio channel characteristics. Consequently, the placement of ABSs significantly affects the quality of service (QoS) experienced by mobile users when accessing the network to request services. Resolving these challenges associated with ABS-based wireless networks is essential to ensuring operational efficiency and delivering reliable, high-quality services to mobile users.
The proposed model in this article effectively solves the placement of ABSs by integrating a network evaluation model that analyzes the wireless environment at a specific time, using channel models such as Rayleigh and two-ray to simulate signal propagation conditions in critical scenarios. Furthermore, the application of the (1+1)-evolution strategy enables the discovery of high-quality solutions within a reasonable timeframe, which is crucial in emergencies where every second counts.
The results demonstrate that the proposed model can improve the probability of successful voice services by an average of 60%, compared to random ABS placements. This advancement not only addresses a highly complex technical problem but also has a direct impact on the ability of rescue teams and victims to communicate in critical situations, potentially saving lives.
[1] R. Shahzadi, M. Ali, H.Z. Khan, M. Naeem, UAV assisted 5G and beyond wireless networks: A survey, J. Netw. Comput. Appl. 189 (2021) 103114, http://dx.doi.org/10.1016/j.jnca.2021.103114.
[2] N. Parvaresh, B. Kantarci, Deep Q-learning-enabled deployment of aerial base stations in the presence of mobile users, in: Proceedings of the 20th ACM International Symposium on Mobility Management and Wireless Access, MobiWac ’22, Association for Computing Machinery, New York, NY, USA, 2022, pp. 73–80, http://dx.doi.org/10.1145/3551660.3560909.

7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable);

G. Rodríguez-Cortés, A. Martínez-Vargas, M. A. Cosío-León, D. M. Martínez, and O. Montiel, ‘2-D deployment of aerial base stations: A simulation model to provide voice communication’, Simulation Modelling Practice and Theory, vol. 139, p. 103048, 2025, ISSN 1569-190X, https://doi.org/10.1016/j.simpat.2024.103048.

8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors;

Any prize money, if any, is to be divided equally among the co-authors of the cited paper.

9. a statement stating why the authors expect that their entry would be the "best",

Our entry, titled "2-D Deployment of Aerial Base Stations: A Simulation Model to Provide Voice Communication," represents an advancement in the application of evolutionary computation to solve a critical real-world problem: restoring communication in disaster-stricken areas by locating ABSs to deploy an aerial temporal network. The proposed solution has been tested on a real-world instance since the geographic coordinates for mobile users and the boundaries of the affected disaster zone are sourced from the town of Tula, Mexico, an area where the communication infrastructure was devastated by severe flooding in September 2021. Our motivation for selecting this case study stems from the collapse of the communications infrastructure caused by the flooding of the Tula River, which made it impossible to alert the personnel at the hospital of the Mexican Social Security Institute (IMSS) in time to carry out an evacuation. At that time, the country was amid the COVID-19 pandemic, and 17 patients died due to the interruption of the oxygen supply, as they were connected to mechanical ventilators in that hospital. The power outage that led to this tragedy occurred when floodwaters rapidly inundated the hospital. Due to climate change, extreme events such as floods are likely to become more frequent.

Our entry aligns with the 17 ONU Sustainable Development Goals (2030 Agenda agreements), specifically, goals 9, 11, and 13. One of the targets in Goal 9 (Industries, Innovation and infrastructure) mentions the development of reliable and resilient infrastructure to support human well-being, especially in developing countries. Consequently, Goal 13 (Climate Action) focuses on strengthening resilience and adaptive capacity to climate-related hazards and natural disasters in all countries. Meanwhile, Goal 11 (Sustainable Cities and Communities) includes a target to significantly reduce the number of deaths and the number of people affected by disasters by 2030, with special attention to protecting the poor and people in vulnerable situations. Also, our entry aligns with the Sendai Framework for Disaster Risk Reduction, which provides member states with concrete actions to safeguard development gains from disaster risks. Within the Sendai Framework, resilience in telecommunications infrastructure is addressed through disaster risk management. It specifically highlights disaster preparedness and post-disaster recovery, emphasizing the importance of maintaining the operational continuity of telecommunications during emergencies, which is critical in rescue coordination (communication among emergency response teams).

Beyond the application proposed in our entry, the aerial temporal network also has potential use cases in scenarios characterized by high network traffic congestion, such as football stadiums or large-scale music concerts, by alleviating congestion and supporting the existing communication infrastructure to enhance the quality of service.

10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression
programming), DE (differential evolution), etc.

Evolution strategies

11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement.

4 December 2024