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High-performance Processors For Disaster Management Simulations
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Received: 30 April 2022 / Modified: 9 June 2022 / Received: 14 June 2022 / Published: 22 June 2022
Natural disasters are crisis situations that endanger human life. During natural disasters, public communications infrastructure is particularly damaged, hampering search and rescue (SAR) efforts, and significant time and effort is needed to restore functional communication infrastructure. SAR is an important element in reducing human and environmental risks in disasters and adverse environments. As a result, there is a need to build a rapid communication network to support SAR efforts in exchanging emergency information. UAV technology has the potential to provide significant solutions to mitigate such catastrophic situations. UAVs can be used to provide flexible and reliable emergency communication backbone and address key disaster risk issues for SAR operations. In this article, we evaluate the network performance of UAV-assisted smart edge computing to accelerate the mission and functionality of SAR because this technology can be implemented in a short time and save most people in the event of a disaster. We considered network parameters such as latency, transit and sent and received traffic, as well as path loss for the proposed network. It has also been shown that network performance has been significantly improved with the proposed parameter optimization, resulting in more efficient SAR missions in disasters and adverse environments.
In recent decades, many countries have suffered heavy casualties and economic losses due to the frequent occurrence of natural disasters. Recently, natural disasters have caused enormous damage, creating a crisis situation that puts people’s lives at risk. One of the main reasons for this huge loss is the lack of an effective disaster relief network management system to support the rapid dissemination of emergency information. Disaster management, search and rescue missions (SARs) and health monitoring are important applications that require high-precision, sometimes rapid localization of objects. During and after emergencies, central management systems and infrastructure, including communication base stations (BS), can be destroyed, resulting in insufficient critical resources and disruption of communication links. It can take days to weeks to restore the backbone of communications to restart telecommunications and internet services. Therefore, it is necessary to use emerging and future technologies to restore the immediate communication network. In addition, during a natural disaster, SAR must communicate with the management center and with the victim. Victims must advertise their location and receive rescue information in a closed system. With low-cost mobility management and robust communications (LoS), unmanned aerial vehicles (UAVs) can act as BSs emergency pilots to serve land users who have lost contact with BS-based equipment. On the ground. Necessary for information dissemination. . The use of UAVs in SAR missions greatly reduces the effort, cost, finances and time spent while saving lives. To address this, UAVs can operate as flying BSs to provide emergency wireless communications services in disaster-affected areas .
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A cluster of UAVs can be used to detect incidents on urban highways to provide first aid to vehicles as described in . UAVs provide rescue teams with valuable information and speed up rescue efforts. Robust routing mechanism ensures communication stability when sending crash information. In  the authors introduced a revolutionary UAV route planning system for the distribution and collection of emergency messages. Mobility and referral capabilities are maximized when you visit an access site to send and collect emergency alerts. In addition,  proposed Wi-Fi networks using UAVs to assist people working in support centers to collect tracking data. The planned network aims to speed up rescue efforts and provide up-to-date disaster information.
The ability of UAVs to carry various materials and travel long distances speaks volumes about their rapid expansion in the civilian sector beyond surveillance and aerial photography. Equipped with electromagnetic sensors, real-time processing modules and advanced communication systems, UAVs represent a new low-cost solution to enhance the existing capacity of public authorities and rescue agencies to identify and locate injured and missing persons during and after. After a natural disaster. . The authors of  presented methods for obtaining information about areas destroyed after a natural disaster. This process involves taking real-time photographs of their location and altitude, and transferring images with flight characteristics to a ground control station to create a three-dimensional hazard map.
Autonomous decision-making in disaster relief networks can only use local information, which can reduce the effectiveness achieved by the game theory approach. Some existing work has turned to graph theory to solve dynamic resource allocation problems with complexity. The authors demonstrate how UAVs equipped with visual cameras can assist in SAR avalanche operations . This process uses Machine Learning (ML) techniques. Images of snowflakes taken by UAVs were first analyzed using pre-trained Normal Network (CNN) to extract the discriminatory features. The authors of  described the method of detecting the human body based on color and depth information from onboard sensors. In addition, the authors present computational models for tracking multiple people and rotating views around targets using a variety of dimensions.
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In addition,  proposed a fully autonomous UAV rescue with real-time human detection on board. Researchers used the Deep Learning (DL) algorithm to identify swimmers in open water. However, the main problems of channel allocation in a static and dynamic environment are twofold: (1) only local knowledge can be used to make self-management decisions and (2) the speed of the process. The combination of algorithms must adapt to the dynamic polarization network. With these two issues in mind, we aim to study the integration of UAU computing to further enhance SAR performance with the integration speed of dynamic networks in disaster areas using local information in decision making.
Recently, UAVs have proven very useful in applications such as smart agriculture , security, power line monitoring, surveying and mapping,  surveillance,  SAR [12, 13, 14]. Transportation  and disaster area coverage . In addition, UAVs play an interesting role in crop control, allowing various activities that were previously only available to farmers . Bidding techniques classify the most suitable UAVs for survivors in UAV and SAR emissions based on the bidding value obtained as the distance between the UAV and the survivor. There is a disaster center where most of the survivors are located, which is important to our efforts. As a result, SAR operations should be organized with a greater focus on the center, reducing priority as the distance from this point increases.
UAVs can be deployed quickly and their coverage can be dynamically changed in disaster communications systems. They provide fast and efficient network support for SAR disaster sites, and real-time information from disaster sites can be sent to the SAR for better rescue of affected individuals . As a result, communication between the Disaster Rescue Command and the SAR is important in the disaster response process, and disasters often disrupt trade networks in disaster areas. In addition, a network of UAVs can be deployed at the center of the disaster area to monitor disaster areas by launching SARs.
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