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Internet of Things

IoT is the term created that explains the fact that several objects used in daily life are connected through the internet, generating data and facilitating daily tasks, being a vast, complex, and adaptive network of devices with sensors, microchips, with processing and communication capabilities that interconnect people, machines, infrastructure, computing capacity, and systems over the Internet with security and privacy [1,14].

In addition to being an important resource for collecting information in Big Data, allowing accessories, cameras, sensors, appliances, phones, drones, and many more devices and things to connect to the network, facilitating the daily life of human life, such as residential security that alert residents in real time, via smartphone, in case of break-in and invasion, are forms of IoT, just as this technology is economically and socially impacting different sectors and contexts, making cities smarter, rationalizing, and flexibilities logistics and transportation of goods, production, even providing remote monitoring of patients, in the context of health, the use of wristwatches that measure the number of steps in the day, heart rate, and many other data on the quality of life and health of the patient-user. In the same sense as allowing agribusiness, the optimized use of inputs, improving energy efficiency, reducing the risks of work and operation in an industrial plant, expanding access to services in the financial sector, enabling new business models based on use for insurers and rental companies [15].

IoT sensors can assure the population of the quality of the water supply, the levels of pollution and radiation in each location, among other applications of equal economic and social impact, since its advent there is already more equipment connected to the internet than people on the planet, where the internet stops connecting only people and starts to connect the things that surround them [16].

The information that IoT devices generate, they also make available a large amount of them, from a smoke detector generating sporadic events to a video surveillance system generating several events per minute, which instead of issuing a movement alert in an area, the vision system can recognize shapes, objects, and people and also inform by voice messages regarding the masked individual, where users cannot deal with a large number of events or alert messages, which can cause the human error and increase the level of risk, since combined with the resources of AI, a system can make analyses and learn in a fraction of a second the behaviors of the device, making recommendations to the user, or even taking specific actions instead of just sending alerts to someone who will make the decision [17].

The technology that uses sensors in fire prevention evolves considerably, given its extreme importance and that this type of tragedy represents a great risk to life and the environment, where one of the main causes of desertification in many countries is related to this type of incident which is also the cause of many tragedies in buildings and factories, since low humidity and high temperatures are two factors that when combined contribute significantly to the beginning and spread of forest fires, which these and other factors can be monitored through intelligent sensors helping to create more efficient prevention strategies [18].

From the collection of data from sensors or geographically distributed third parties, it is possible to transform this large volume of data, which can be handled with Big Data, into intelligent and accurate predictive recommendations and strategic information, leveraging the best of both worlds in terms of processing, analysis, and learning much faster than the human dimension. IoT sensors can be installed on almost any imaginable object, where through a mesh of mobile sensors and fixed sensors, they can be located in vehicles, enabling the construction of complete coverage to monitor the climate and atmospheric data of a given region. This data can be sent to cloud-based analytical software that performs real-time processing based on intelligent predictive forecasting algorithms, the main data being transmitted to a system that can trigger alerts when highly dangerous fire conditions occur, managing teams or smart devices, such as drones, to fight outbreaks of fire before they cause destruction and spread [15].

In very large areas, it can be difficult to take preventive actions; however, with the use of sensors, this is a possible task, identifying the fire spots in advance to prevent the fire from spreading and causing greater tragedies, even considering that certain types of sensors in the ability to identify the abnormal rise in temperature or even the presence of CO and C02. The great advantage is that the sensors can indicate the location of the start of the fire allowing the firefighting teams to be much more efficient and the control to be carried out even in its initial phase. Still considering the possibility of integrating AI solutions that make the prevention systems safer and more efficient in the management of fire risks, derived from the continuous data collection of the IoT devices and the use of sensors that communicate automatically allowing the emission of messages and alerts that indicate the conditions of the monitored area, as well as the number of people who are in the location. In buildings and industries, these devices can be connected to alarm systems and firefighting systems, which can be vital in saving lives, since knowing where a fire is occurring is invaluable, which through sensors IoT confirm this accurately, showing not only where the fire started but where it is spreading and how quickly [18,19].

In earthquake scenarios, drones can be used in mapping-affected areas, assessing damage, and assisting in the search for missing persons, also using drones to record damage, covering areas where hours would be spent traveling on the ground [19].

More than registration, drones can be used to support rescue teams and to monitor structures, registering cracks and cracks in a wall, still considering functions such as radars and piezometers for the remaining daily monitoring. In the same sense that drones assist in search and rescue activities, being essential due to the difficulty of locomotion in a certain type of terrain, serving as an extension of the vision of the rescuers [20].

More than to register environmental accidents, sensors and drones can be used for inspections and monitoring of structures with a focus on reducing the risks of material and personal accidents, both for the populations surrounding the developments and for the workers involved in the productive activities In this sense, IoT technology is in constant technical evolution, opening up new' possibilities for future uses, contributing to the prevention of accidents, reducing the response time to a disaster, and minimizing the effects on those affected [21].

The Use of IoT and AI for Risk and Disaster Management

The technological change that IoT generates for people’s lives goes beyond the daily facilities as seen in the automation of a smart house, providing connectivity to objects and making room for intelligent commands in the various daily tasks, in addition to significantly increasing productivity at w'ork, improving public and private security conditions, improving urban mobility as well as streamlining industrial processes, among others [22].

With IoT, it is possible to obtain data on human trends that w'ere previously unimaginable, from generic information on behaviors that make up the volume of data known as Big Data, making human difficulty regarding the translation of all this data accumulation with traditional methods, since, in addition to this large amount, it is difficult to guess w'hat information will be found, which by AI is possible due to natural language processing, generating valuable values and insights. Combining these aspects, the computer is shaped according to the information it finds, where throughout the process, it changes with the results delivered, through machine learning [23].

This process of generating, analyzing data, and creating insights can be used by several sectors, such as agribusiness, commerce, industry, health, transportation, public services, among others. Just as the IoT together with AI contribute to disaster management to be qualified by employing new technologies to identify situations and act preventively, avoiding the loss of assets and resources, as well as people’s lives [24].

These technologies range from sensors to algorithms capable of reading and analyzing the information captured, making it possible to offer reliable data for the management of these disasters, provided that the use of wireless sensor networks, adopting sensor networks based on IP as well as using emerging standards for IoT, for data collection and the use of machine learning techniques oriented on top of the information collected from these sensors for the prediction of natural disasters are viable options, due to such technological trends have shown over time to be promising aggregating in the forecast natural disasters and environmental monitoring task [25].

In the industrial context, natural disasters are a serious problem since until a few decades ago, there was no way to deal with them, since everything that could be done to reduce risks was related to backup ensuring access to data, the construction of safe structures for installing companies’ machinery and purchasing insurance, ensuring possible material damage [26].

Thus, with the advent of IoT, it generated an impact allowing for the prior knowledge of risks and disasters, which represents a competitive advantage, from an industrial and business point of view, since IoT devices work under less than ideal conditions, in relation to scenarios where there is a very weak wireless connection or few sources of energy, with the need to use technologies such as LoRa (Low Power) to use and perform triage and detect the beginning of tectonic activities with precision, for example, with the IoT changing how to face problematic situations and can even save lives [27].

As the batteries of these devices do not need to be replaced frequently, these sensors can be installed in remote areas of difficult access, in a scenario of a flood or tsunami, the pressure and humidity sensors perform detection of a submerged bridge, giving a preview of the situation and transmit the information to an environmental monitoring center, which will have knowledge of the time that occurred, still being able to warn the population about possible alternative escape routes as well as having the intensity of the damage [28].

AI is associated with intelligent objects that can transform disaster prevention into a much more accurate task, using sensors and drones, and even robots, being a way to better coordinate actions in areas of risk in the same way as ensuring that service providers may have the necessary capabilities to understand the situations in which they are inserted more quickly [29].

The fire brigade can process the heat areas in a building with intelligent software to trace more efficient routes through computer vision and can reach victims faster, since these resources allow professionals to be placed at less risk and can do their job with greater chances of success. The response to AI disasters is a trend with a concern forgetting a complete view of the problems before taking action, since this complete view can help in the definition of smarter plans by assisting in the preparation with the right equipment and training aiming to mitigate the effect of disasters [30].

As with the use of machine learning, which is an aspect of AI, in online mining with a focus on identifying tweets made in emergencies, forwarding this information to networks of volunteers and governments, acting on the verification of which areas were most affected and what were the needs of the people in those places, promoting actions of teams that are able to send resources, such as water and food, in the same sense as addressing problems related to damaged infrastructure more quickly, nurturing the latest information about the case [31].

In addition, it is important to highlight the great importance of investing in methodologies capable of transmitting a large flow of information with low memory consumption of the devices as well as low abstraction. These characteristics are essential for sending and receiving a massive flow of data in disaster situations [32-37].

In the same sense, at times of the year, such as summer, tropical storms are approaching, with tropical countries like Brazil in certain locations causing the population and public service agencies to be on the alert, with relations to heavy rains common at this time that cause numerous inconveniences, from interruptions in the supply of crucial services such as water and electricity, causing cities to considerably alter traffic in relation to floods that destroy neighborhoods, to power outages after a summer storm cause ripple effects and contribute to the feeling of chaos in the general population. However, this type of chaos scenario can be avoided with organization, planning, and preparation in advance, with respect to the use of IoT sensors that can be used to monitor a variety of climatic conditions such as raindrops or snow, determining and anticipating possible situations of chaos with the help of a system that processes data histories providing forecasts, in the same way as the use of an AI system that helps to identify and order the services that will be performed and the professionals that need to be scaled assessing their skills and determining your location relative to the emergency, prioritizing calls, sending the right people closest to the location and resolving the problem as quickly as possible [38,39].

Thus, the union of IoT with AI for disaster management is a way to qualify these processes, ensuring that a company or even the government or state is prepared for unforeseen events, since they have technological resources such as geological risk sensors, which can predict landslides, sinking and burning in the field, forecasting storms and snowfalls, helping to prevent these activities from causing damage to society. Still considering the need for a tool that allows managers to efficiently organize emergency tasks, following the parameters of the institution itself or regulatory agencies and selecting what should be prioritized, based on clear and predefined rules.

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