ABSTRACT
In December 2019, several cases of pneumonia caused by SARS-CoV-2 were identified in the city of Wuhan (China), which was declared by the WHO as a pandemic in March 2020 because it caused enormous problems to public health due to its rapid transmission of contagion. Being an uncontrolled case, precautions were taken all over the world to moderate the coronavirus that undoubtedly was very deadly for any person, presenting several symptoms, among them we have fever as a common symptom. A biosecurity measure that is frequently used is the taking of temperature with an infrared thermometer, which is not well seen by some specialists due to the error they present, therefore, it would not represent a safe measurement. In view of this problem, in this article a thermal image processing system was made for the measurement of body temperature by means of a drone to obtain the value of body temperature accurately, being able to be implemented anywhere, where it is intended to make such measurement, helping to combat the spread of the virus that currently continues to affect many people. Through the development of the system, the tests were conducted with various people, obtaining a more accurate measurement of body temperature with an efficiency of 98.46% at 1.45 m between the drone and the person, in such a way that if it presents a body temperature higher than 38° C it could be infected with COVID-19. © 2023 IEEE.
ABSTRACT
The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.
ABSTRACT
Covid-19 has brought about the development of the use of technological applications and initiatives to control the situation. The aim was to effectively monitor and eventually treat patients and facilitate the efforts of medical staff. This paper is the result of research into the possibilities of solving the problems caused by the Covid-19 pandemic by applying selected logistics technologies. The paper provides a new perspective on technologies and discusses possibilities for future research. The goal of the study is based on relevant existing research which examines in detail how five selected technologies can help in the fight against the pandemic. It defines the potential practical application of these technologies in the context of threats to public health. The main findings of this paper concern the application of logistics technologies considering these essential factors. The research characterizes the problems associated with using the proposed technologies, such as costs, time, legislative constraints, safety, and ethics. Proposals for the practical application of logistics technologies in solving pandemic-related problems must accept not only the legal framework but also the socio-ethical dilemmas that these technologies present. © 2023 Vladimír Klapita et al.
ABSTRACT
This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
The global COVID-19 pandemic forced the construction industry to a standstill. In the wake of the pandemic, this sector must be prepared to make bold, innovative moves to prepare for the future. Over the past few years, the use of drones and robotics has expanded with many commercial uses, including in the construction industry. Drone-driven automation has an enormous impact in improving productivity and reducing cost and schedule overruns. The use of drones, along with the application of Internet of Things (IoT) and robotics, can make a significant impact on the supply chain and improve inventory accuracy, leading to faster and more cost-effective building projects. This paper will propose and statistically substantiate an optimization model for supply chain management through the accelerated use of drones and Artificial Intelligence (AI) in the post-pandemic era. The use of smart devices and IoT will allow warehouse managers to have real-time visibility of the location and inventory tracking, as well as enabling warehouse workers to access information without being physically present. Cutting-edge drone technology can quickly perform inspections to make inventory control more economical and efficient. While they are certainly not a perfect fit for every building surveillance task, drones have many advantages for probing buildings in search of leaks, performing aerial surveys, and dealing with security issues more cost-effectively than manual procedures, thereby leading to improved communication and collaboration between different stakeholders. This paper includes a real-life case study and dynamic mathematical model to demonstrate how this approach results in a project's materials becoming visible, traceable, and easily tracked from end to end. © 2023 by the authors.
ABSTRACT
Artificial intelligence is becoming more prevalent across diverse disciplines, and aerial vehicles are increasingly becoming "Unmanned”. It is beneficial when residents might otherwise be in danger, such as during COVID-19 medicine delivery, gathering information about the enemy, or using it in agriculture. This study aims to provide a scientometric assessment of the latest research centres, patterns, and global reach of UAVs from 2007 to 2022. The study uses bibliographic information downloaded in CSV format from Scopus to examine the in-depth visualization of the index item's properties. In addition to examining article expansion, field classifications, global dispersion, citation analysis, and the impact of the institutions and writers, the study examines UAV applications distributed throughout the world. To analyse term co-occurrence, we use a Java-based program called VOSviewer, which lists hubs and the latest innovations in UAV research. © Author (s) 2023.
ABSTRACT
The Internet of Drones (IoD) allows drones to collaborate safely while operating in a restricted airspace for numerous applications in Industry 4.0 world. Energy efficiency and sharing sensing data are the main challenges in swarm-drone collaboration for performing complex tasks effectively and efficiently in real-time. Information security of consensus achievement is required for multi-drone collaboration in the presence of Byzantine drones. Byzantine drones may be enough to cause present swarm coordination techniques to collapse, resulting in unpredictable or calamitous results. One or more Byzantine drones may lead to failure in consensus while exploring the environment. Moreover, Blockchain technology is in the early stage for swarm drone collaboration. Therefore, we introduce a novel blockchain-based approach to managing multi-drone collaboration during a swarm operation. Within drone swarms, blockchain technology is utilized as a communication tool to broadcast instructions to the swarm. This paper aims to improve the security of the consensus achievement process of multi-drone collaboration, energy efficiency, and connectivity during the environment's exploration while maintaining consensus achievement effectiveness. Improving the security of consensus achievement among drones will increase the possibility and stability of multi-drone applications by improving connectivity and energy efficiency in the smart world and solving real environmental issues.
ABSTRACT
Industrial applications of drones have increased during the past few decades. Automation has been the major factor behind the increasing usage of Unmanned Aerial Vehicles (UAVs). At first, UAV applications were limited to military applications or other such applications requiring government permissions. Nevertheless, industrial applications of drone usage increased in different industries including health (for COVID or other such pandemics), institutions or universities, weather monitoring, and many more. On the other hand, the increased usage of drones has led to increasing malicious attacks on drones. Therefore, it is an alarming issue affecting information. Owing to such related concerns, in this chapter, the Blockchain-based security methods or privacy mechanisms are presented. The overall objective of this chapter is to discover the vulnerabilities in Unmanned Aerial Vehicle (UAV) and then to analyze various solutions to overcome the security problems using blockchain technology. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
Drones operate on electric batteries and not on gasoline, so the eco-friendly role of drones has recently attracted a lot of attention. Thus, this study was designed in order to investigate differences in behavioral intentions, such as intention to use, word-of-mouth, and willingness to pay more, according to demographic characteristics and past experiences in the field of eco-friendly drone food delivery services. Data were collected from 422 potential consumers of eco-friendly drone food delivery services in South Korea. The data analysis results indicated that females are more willing to pay extra than males are, respondents who were in their 50s had higher word-of-mouth intention than other generations, marital status showed significant differences in willingness to pay more and intentions to use, and there was a difference in willingness to pay more and word-of-mouth with regards to monthly income. In addition, respondents who had previously heard of drone food delivery services had higher averages with willingness to pay more and intentions to use as opposed to respondents who had not heard of them, and respondents who had experience controlling drones were willing to pay additional fees when they used eco-friendly drone food delivery services. The results of this study would be a great assistance for executives who will operate eco-friendly drone food delivery services.
ABSTRACT
COVID-19 is highly contagious in nature. It marked a grievous impact on the world's social and economic status and disrupted the other related domains as well. Global mental health is debatable in present scenario. Travel and Tourism industry is one of the hardly hit sectors which clearly and badly influenced the aviation industry. In this article, we highlight some of the major global sectors which are highly affected by the pandemic, including social and financial turmoil caused by COVID-19, healthcare front, environment, education, and travel. A reasonable weightage is given to situation in India while discussing about the different impacts. Since the outbreak, researchers have been working feverishly to leverage a broad range of technologies to tackle the global threat. The Internet of Things (IoT) is one of the forerunners in this field. The Internet of Things (IoT) has gained popularity as a new research field in a variety of academic and industry fields in recent years, particularly in healthcare. This article investigates and highlights the overall applications of the well-proven IoT tools and technologies in all the COVID-19 impacted domains by providing a perspective roadmap to combat this global threat. Various myths or misconceptions regarding COVID-19 have also been discussed and explained logically. © 2023 Scrivener Publishing LLC.
ABSTRACT
This paper shows the importance of technology in mitigating the spread of Covid-19 which has significantly changed the lives of people, affecting health, livelihood, and the global economy due to the closure of international borders. It has promoted social distancing and increased the need for measures that can eliminate the impact of COVID-19 related issues and outcomes. The research investigates the ways technology can benefit to support teams in preventing Covid-19. It also aims to examine the main advantages of drones during Covid-19. In addition to suggesting the use of drones to fight the Covid-19 pandemic. The inability of some countries to cope with the virus and the effective use of drones to deal with it highlights its importance. However, there is a need to adjust some cultures to the use of drones during the coronavirus crisis. Based on the review of literature, it indicated that people couldn't eliminate the virus impact yet, but they could alleviate the damage they caused. The current research has used interviews to collect data from five organizations involved in the mitigation of unknown-unknown risks, including the Ministry of Health & Community Protection, Dubai Executive Council, DEWA, Melaaha Drones, and ArabDrones. Moreover, a face-to-face interview was conducted by phones and e-mail conversations. As well as a literature review has helped in exploring the unknown-unknown risk method along with the use of Artificial Intelligence such as drones to assist in combating the Covid-19 pandemic. The descriptive qualitative research design has helped to test the hypothesis regarding its ability of drones to mitigate the unknown-unknown risk of Covid-19. The research results part indicated that drones are vital constituents of contingency planning. They have proven their value by going for the extra mile in saving costs and lives by incorporating drones technology in minimizing the effects of the crisis which was illustrated with some real examples. Also, the paper has proven the hypothesis by showing another real example on a drone company like ArbaDrone and how they assess, plan and mitigate unknown-unknown risks by following such a specialized program called RIO Project Program to measure and minimize the crisis impacts in the running company. Thus, drones have significantly helped countries such as the UAE to deal with the risks against Covid-19 through the elimination of infection risks with about 96% accuracy via computerized tomography scans. Finally, since the drones have contributed to international success in mitigating the Covid-19 pandemic and having an essential role in fighting the spread of the virus, so, it is very necessary to re-enforce the drone's experience to enhance the fight against coronavirus attracting other tools. Furthermore, extra studies still needed to give more bigger pictures about the impact of this coronavirus statistically to have more information about its influences on the global market. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The study's goal is to create a detector that detects and analyses whether pedestrians or individuals in public gatherings are maintaining social distancing. Drone-shot videos, live webcam feeds, and photographs are all kinds of input for the detector. With no human intervention, Dynamic Detection through live stream provides safety and simplifies monitoring of social distance. The webcam input can be integrated with an external webcam or a drone's camera. Furthermore, the YOLOv4 algorithm is used for the data set for the initial phase ofobject detection, identifying various items in each frame. The recognized objects are narrowed down to humans, and the Euclidian distance between one data point and every other data point is determined The Euclidian distance determines if they are maintaining the minimal distance between them or not by depicting them with a colored border box. Euclidian distance assists in detecting if they are keeping the minimal distance between them or not, as shown by a coloredboundary box, red for unsafe and green for safe, with an indication reflecting the number of people in danger. © 2022 IEEE.
ABSTRACT
Drone technology is widely available and is rapidly becoming a crucial instrument in the functions of businesses and government agencies worldwide. The demand for delivery services is accelerating particularly since the Covid-19 pandemic. Both companies and customers want these services to be efficient, timely, safe, and sustainable, but these are major challenges. Last-mile delivery by lightweight short-range drones has the potential to address these challenges. However, there is a lack of consistency and transparency in assessing and reporting the sustainability of last-mile delivery services and drones. This paper presents a critical review of published assessments (specifically lifecycle assessment and circularity). The study reveals a lack of comprehensive studies, and a need to examine composite and battery manufacturing developments and provides key considerations for future study development. © 2022 Mitchell et al.
ABSTRACT
People's failure to maintain a social distance is causing the COVID19 virus to spread. We have used the drone thermal images for a maximum of 10 km of coverage to detect temperature and reduce virus spread areas. The part of the work is based on utilizing disinfectant spraying drones, disinfectant testing with the guidance of doctors, setting the path planning of drones for surveying the temperature of people, and monitoring the infected place using GPS. When the thermal camera of the drone detects the temperature values using remote sensing images, the drone covers crowded places like hospitals, cinemas, and temples using remote sensing images. One drone model is designed to provide present results using thermal images. The Proposed drone can cover an affected area of up to 16,000 square meters per hour for capturing remote sensing images. It predicts affected areas using faster CNN algorithms with 2100 thermal images. Thermal mapping is used to monitor the social distance between people, alert people that a virus is spreading, and reduce the risk factor of people's movement. In this paper, remote sensing images are analysed and detect higher temperature areas using thermal mapping (Messina and Modica in Remote Sensing 12:1491, 2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.
ABSTRACT
Since the COVID -19 epidemic has nearly brought about global catastrophe, every chance to make things better must be considered. One such technique for improvement is airborne decontamination. Researching this method's efficacy in the pandemic is vital since it can be used for surface cleaning of bigger areas. There are numerous instances of using drones to disinfect areas affected by epidemics, but best practices and factors affecting effectiveness have not yet been found. The adaptable uses of agricultural drones are evident from reports about utilizing drones for disinfection during a pandemic. The authors of this study calculated the potential amount of disinfectant fluid per unit area using various parameters for fly speed, flight altitude, and flow rate. As a result, by adjusting the settings, a range of disinfectant concentrations per unit area can be provided. Even though the results create a lot of new queries, they can be used to determine appropriate flying characteristics based on various disinfection liquids. © 2022 IEEE.
ABSTRACT
The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.
ABSTRACT
PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.
ABSTRACT
Autonomous unmanned aerial vehicles (UAVs) have witnessed a rapid increase in their utilization in various applications and will continue to do so in the coming decades. These UAVs, also known as drones, are designed to either assist humans or perform tasks that involve people. Drones of today have grown to be faster and less expensive by integrating several technologies, supported by hybrid algorithms, and perform various tedious, challenging, filthy and hazardous tasks. The deployment of machine learning and other AI-based algorithms enhances drones' autonomous and vision capabilities. Today, part of an effort to curtail the spread of COVID-19, this research has designed, developed and built a mobile disinfectant dispenser based on autonomous quadrotor UAV. It is a 'flying dispenser', able to detect a person's hand gestures from afar, based on machine learning (ML), to fly and maneuver towards the person and finally spray disinfectant on his/her hand. In order to identify various hand motions for maneuvering, this research studies and improves the ML algorithms and carries out various experiments to improve the drones' response time and maneuvering performance, for the final objective of taking precautions to protect humans from Covid-19. © 2022 IEEE.