# Bot-Based Emergency Software Applications for Natural Disaster Situations
## 5 Related Work
Moreover, they do not provide detail about the architecture, the components, or the performance of the application. The work in [30] presents a prototype of a spontaneous voluntary coordination app. It uses PostgreSQL as the webserver. However, it has not been tested on a large scale. The authors present results based on the simulation of 200,000 agents, but they do not report information about how the application behaves under a high load of user requests, fault-tolerant deployment, scalability, or possible bottlenecks. Similar to [30], the authors in [31] present an application to organize pre-qualified volunteers (e.g., volunteers with medical skills). The application allows enrolling and characterizing volunteers, and through a survey, the application predicts the willingness of users to participate in a rescue of which they are notified by a smartphone application. As in the previous case, the paper does not give information about the architecture of the application, implementation, or about the performance evaluation. There are applications for emergency management designed for P2P networks [4; 32]. However, managing a P2P network requires implementing a distributed hash table (DHT) and communication protocols such as Pastry or Chord. Moreover, high peaks of workloads are difficult to manage, tending to saturate not only computation resources but also communication channels as messages travel from one peer to another until they reach their destination. A classification model is used in [33] as part of a conceptual framework for the development of emergency applications. The authors evaluate the proposal using a model to select, classify, and prioritize tweet messages obtained from the Mexico 2017 earthquake for rescue operations. The authors do not report about the training time and about the execution time of the model. Moreover, the ability of the model to fit other datasets is not discussed. Some applications collect data from real-time sensors and drones [34; 35]. Sensors have been used in fixed [36; 37; 38] and mobile sensor networks [39; 40] for environmental monitoring and for planning rescue operations. In particular, drones have been proven to be a useful tool to obtain a map to support searching in a post-disaster scenario [41]. In the same line, the work presented in [42] simulates disaster scenarios and analyzes data by using the unmanned aerial vehicle technology +BIM model's monomer and visual information query. The authors propose using these technologies to look at the disaster data for planning and early warning. However, these kinds of applications do not include the participation of volunteers, the managing of rescue tasks, or the communication with victims. Nowadays, bot-based software applications have been widely used in different areas such as journalism [43], where bots are used to personalize the information given to users. A user interacts with the bots, which delivers increasingly specific information through the use of questionnaires and machine learning techniques. These bots are typically implemented on Twitter, Facebook, and Telegram platforms. Bots are also used in the supply chain process [44] to analyze the user queries and provide information on the queried orders and supplies; they are also used in e-commerce and retail companies [45; 46] where users can register, and then the bot shows catalogs, guides, shopping carts, reviews, tracks the purchases, and generates purchase orders; for medical assistance [11; 47], virtual robots represented in a bot are used to communicate with patients and assist them in their routine needs; and they are also used for data collection [13; 48; 49]. In particular, the bot presented in [48] is designed to collect and validate real-time crowd-sourced flood users reports via social media (Messenger of Facebook bot or Twitter) to enhance the city's resilience to extreme weather events. The system is deployed on Amazon Web Server (AWS) to support fault tolerance and scalability.