Do drones have a realistic place in a pandemic fight for delivering medical supplies in healthcare systems problems?

Deep Reinforcement Learning-Based Multirestricted Dynamic-Request Transportation Framework

Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source-destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor-critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

Exploring the transformative role of drone technology in advancing healthcare delivery in Africa; a perspective

This perspective article delves into the transformative potential of drone technology in revolutionising healthcare delivery in Africa. The continent faces numerous challenges in providing timely and efficient medical services to its vast and diverse population, compounded by geographical barriers, inadequate infrastructure and limited access to medical facilities. Amidst these challenges, the integration of drone technology emerges as a promising solution, offering unprecedented opportunities to overcome longstanding obstacles and improve healthcare accessibility across Africa. Drawing from existing drone-based healthcare initiatives in Africa, the article explores various applications of drones in healthcare delivery. These encompass but are not limited to, delivering vaccines, medications, blood samples, diagnostic tools and medical personnel to remote locations in a timely and cost-effective manner. Furthermore, the paper examines the operational challenges and regulatory considerations in deploying drone technology for healthcare and the ethical implications surrounding privacy and security.

Survey on Path Planning for UAVs in Healthcare Missions

This article presents a comprehensive review of the state-of-the-art applications and methodologies related to the use of unmanned aerial vehicles (UAVs) in the healthcare sector, with a particular focus on path planning. UAVs have gained remarkable attention in healthcare during the outbreak of COVID-19, and this study explores their potential as a viable option for medical transportation. The survey categorizes existing studies by mission type, challenges addressed, and performance metrics to provide a clearer picture of the path planning problems and potential directions for future research. It highlights the importance of addressing the path planning problem and the challenges that UAVs may face during their missions, including the UAV delivery range limitation, and discusses recent solutions in this field. The study concludes by encouraging researchers to conduct their studies in a realistic environment to reveal UAVs' real potential, usability, and feasibility in the healthcare domain.

Modelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices

The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device's battery should be replaced in order to ensure that the battery is not completely drained before it is replaced.

Elevating Healthcare: Rapid Literature Review on Drone Applications for Streamlining Disaster Management and Prehospital Care in Saudi Arabia

Saudi Arabia's health sector faces pressing challenges in disaster and prehospital care delivery, such as prolonged response times, limited access to remote areas, and strained medical resources. Integrating drone technology has emerged as an innovative approach to address these challenges and revolutionize healthcare delivery. Drones can significantly enhance response times, increase access to underserved areas, and reduce the burden on existing medical infrastructure. A detailed analysis of global case studies demonstrates the successful use of drones in healthcare delivery, emphasizing the importance of regulatory frameworks and public-private partnerships. These examples provide valuable insights into Saudi Arabia's health sector transformation. The potential benefits of integrating drone technology include improved patient outcomes, increased efficiency, and cost savings. To ensure the successful implementation of this transformative approach, it is crucial to establish clear regulatory guidelines, invest in research and development, and foster collaboration between the government, private sector, and healthcare stakeholders. The aim of this study is to explore the potential of drone technology in transforming healthcare delivery in Saudi Arabia, particularly within disaster response and prehospital care services.

Where is the bottleneck? Drones and the paradoxes of digitising medical supplies in Ghana's landscapes of care

Implemented in several African countries, medical drones have emerged as a major infrastructural innovation in national healthcare systems and are widely hailed for improving, if not revolutionising, access to medicine and care for rural populations. Being based on digitally driven, autonomous aviation systems, drones are part of wider efforts to use digital technologies in health systems. In this article, we explore the paradoxes that emerge from definitions of logistics as the bottleneck of quality healthcare. Based on ethnographic research in Ghana, we explore the ways in which drone systems have been built up and justified by private and political actors and used by pharmacists and other healthcare professionals along the supply chain as serving emergencies. However, they have transformed the existing landscape of medical supply chiefly because of the multiple ways in which emergencies are defined. We find that while the introduction of drones has dynamised supply chain processes but also reveals structural bottlenecks, e.g. the lack of medical products and malfunctioning institutions. Situated at the interface of critical studies on infrastructures and medical anthropology, our article contributes to the thriving scholarship on digital innovation in healthcare.

Drone-as-a-Service (DaaS) for COVID-19 self-testing kits delivery in smart healthcare setups: A technological perspective

Drones have gained increasing attention in the healthcare industry for mobility and accessibility to remote areas. This perspective-based study proposes a drone-based sample collection system whereby COVID-19 self-testing kits are delivered to and collected from potential patients. This is achieved using the drone as a service (DaaS). A mobile application is also proposed to depict drone navigation and destination location to help ease the process. Through this app, the patient could contact the hospital and give details about their medical condition and the type of emergency. A hypothetical case study for Geelong, Australia, was carried out, and the drone path was optimized using the Artificial Bee Colony (ABC) algorithm. The proposed method aims to reduce person-to-person contact, aid the patient at their home, and deliver any medicine, including first aid kits, to support the patients until further assistance is provided. Artificial intelligence and machine learning-based algorithms coupled with drones will provide state-of-the-art healthcare systems technology.

A bi-objective optimization model for the medical supplies' simultaneous pickup and delivery with drones

In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.

Optimization of Medication Delivery Drone with IoT-Guidance Landing System Based on Direction and Intensity of Light

This paper presents an optimization of the medication delivery drone with the Internet of Things (IoT)-Guidance Landing System based on direction and intensity of light. The IoT-GLS was incorporated into the system to assist the drone’s operator or autonomous system to select the best landing angles for landing. The landing selection was based on the direction and intensity of the light. The medication delivery drone system was developed using an Arduino Uno microcontroller board, ESP32 DevKitC V4 board, multiple sensors, and IoT mobile apps to optimize face detection. This system can detect and compare real-time light intensity from all directions. The results showed that the IoT-GLS has improved the distance of detection by 192% in a dark environment and exhibited an improvement in face detection distance up to 147 cm in a room with low light intensity. Furthermore, a significant correlation was found between face recognition’s detection distance, light source direction, light intensity, and light color (p < 0.05). The findings of an optimal efficiency of facial recognition for medication delivery was achieved due to the ability of the IoT-GLS to select the best angle of landing based on the light direction and intensity.

Design of a Service for Hospital Internal Transport of Urgent Pharmaceuticals via Drones

The internal transport of medical goods in a hospital heavily relies on human resources that carry the materials on foot. Such mode of transport may be affected by inefficiencies, e.g., due to bottlenecks, and other logistic challenges. Thus, it may benefit from the use of unmanned aircraft systems in several aspects. Such a scenario introduces specific criticalities for healthcare organizations in densely populated areas and below congested airspace, such as the Milan metropolitan area. The authors applied a co-creation methodology to design a highly automated drone service for the delivery of pharmaceuticals at San Raffaele Hospital, Milan, Italy. The needs of the main users were identified by means of semi-structured interviews and visualization material. Based on those outcomes, a drone service was designed and validated with the main users. It emerged that the main gain point of such a service would be increasing hospital logistics efficiency. The risks tied to the operations (e.g., tampering of the delivery container) were evaluated and appropriate mitigations were identified (e.g., use of tamper-evident seals or mechatronic locks). The information required by the digital system offering the needed logistics functions was analyzed for future development. Recent conceptual and regulatory advancements in the field of Urban Air Mobility (UAM) in Europe were elaborated to outline the digital ecosystem in which aviation and non-aviation actors would exchange information to ensure operations’ efficiency, safety and regulatory compliance.

How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID

Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research related to faster obstacle avoidance and new types of hybrid attitude controllers to balance the agility and the power consumption.

Advanced Air Mobility: Opportunities and Challenges Deploying eVTOLs for Air Ambulance Service

Advanced air mobility (AAM) is a broad concept enabling consumers access to on-demand air mobility, cargo and package delivery, healthcare applications, and emergency services through an integrated and connected multimodal transportation network. While a number of technical and social concerns have been raised about AAM, early use cases for emergency response and aeromedical transport may be key to demonstrating the concept and building public acceptance. Using a five-step multi-method approach consisting of preliminary scoping, modeling performance metrics, developing baseline assumptions, analyzing scenarios, and applying a Monte Carlo sensitivity analysis, this study examines the potential operational and market viability of the air ambulance market using a variety of aircraft and propulsion types. The analysis concludes that electric vertical take-off and land (eVTOL) aircraft could confront a number of operational and economic challenges for aeromedical applications compared to hybrid vertical take-off and land (VTOL) aircraft and rotorcraft. The study finds that technological improvements such as reduced charge times, increased operational range, and battery swapping could make the eVTOL aircraft more reliable and cost-effective for aeromedical transport.

Effective and Safe Trajectory Planning for an Autonomous UAV Using a Decomposition-Coordination Method

In this paper, we present a Decomposition Coordination (DC) method applied to solve the problem of safe trajectory planning for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The purpose of this study is to make the UAV more reactive in the environment and ensure the safety and optimality of the computed trajectory. In this implementation, we begin by selecting a dynamic model of a fixed-arms quadrotor UAV. Then, we define our multi-objective optimization problem, which we convert afterward into a scalar optimization problem (SOP). The SOP is subdivided after that into smaller sub-problems, which will be treated in parallel and in a reasonable time. The DC principle employed in our method allows us to treat non-linearity at the local level. The coordination between the two levels is achieved after that through the Lagrange multipliers. Making use of the DC method, we can compute the optimal trajectory from the UAV’s current position to a final target practically in real-time. In this approach, we suppose that the environment is totally supervised by a Ground Control Unit (GCU). To ensure the safety of the trajectory, we consider a wireless communication network over which the UAV may communicate with the GCU and get the necessary information about environmental changes, allowing for successful collision avoidance during the flight until the intended goal is safely attained. The analysis of the DC algorithm’s stability and convergence, as well as the simulation results, are provided to demonstrate the advantages of our method and validate its potential.

Towards Smart Healthcare: UAV-Based Optimized Path Planning for Delivering COVID-19 Self-Testing Kits Using Cutting Edge Technologies

Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic since late 2019 and has affected all forms of human life and economic developments. Various techniques are used to collect the infected patients’ sample, which carries risks of transferring the infection to others. The current study proposes an AI-powered UAV-based sample collection procedure through self-collection kits delivery to the potential patients and bringing the samples back for testing. Using a hypothetical case study of Islamabad, Pakistan, various test cases are run where the UAVs paths are optimized using four key algorithms, greedy, intra-route, inter-route, and tabu, to save time and reduce carbon emissions associated with alternate transportation methods. Four cases with 30, 50, 100, and 500 patients are investigated for delivering the self-testing kits to the patients. The results show that the Tabu algorithm provides the best-optimized paths covering 31.85, 51.35, 85, and 349.15 km distance for different numbers of patients. In addition, the algorithms optimize the number of UAVs to be used in each case and address the studied cases patients with 5, 8, 14, and 71 UAVs, respectively. The current study provides the first step towards the practical handling of COVID-19 and other pandemics in developing countries, where the risks of spreading the infections can be minimized by reducing person-to-person contact. Furthermore, the reduced carbon footprints of these UAVs are an added advantage for developing countries that struggle to control such emissions. The proposed system is equally applicable to both developed and developing countries and can help reduce the spread of COVID-19 through minimizing the person-to-person contact, thus helping the transformation of healthcare to smart healthcare.

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.

Role of additive manufacturing in medical application COVID-19 scenario: India case study

This paper reviews how the Additive Manufacturing (AM) industry played a key role in stopping the spread of the Coronavirus by providing customized parts on-demand quickly and locally, reducing waste and eliminating the need for an extensive manufacturer. The AM technology uses digital files for the production of crucial medical parts, which has been proven essential during the COVID-19 crisis. Going ahead, the 3D printable clinical model resources described here will probably be extended in various centralized model storehouses with new inventive open-source models. Government agencies, individuals, corporations and universities are working together to quickly development of various 3D-printed products especially when established supply chains are under distress, and supply cannot keep up with demand.

Techno Trend Awareness and Its Attitude Towards Social Connectedness and Mitigating Factors of COVID-19

While COVID-19 has taken a toll on many professions and livelihood of all walks of lives, technology has amplified its intrusion to ease the necessities. Innovative technology, therefore, has improved the glitches and provided the software to adhere to these new normal. However, individuals' awareness and attitude toward the advancements of these technological trends need to be addressed. Although the government has taken measures to prevent and curb the growing cases for COVID-19 with the help of technology, the support from the individuals would depend mostly on their level of awareness and the attitude toward those measures. The present qualitative study explored the techno trend awareness, perception and attitudes of techno experts and technical professionals toward social connectedness and mitigating factors of COVID-19. Besides, it also explained individuals' shift toward virtual interaction to maintain social connections during the pandemic. The thematic analysis generated four prominent themes. Social Connectedness, emphasized on the emotional connections that created a positive feeling of belongingness. Technological Advancement provided three sub-themes highlighting perception, techno trend awareness and desirable attitudes toward the mitigation of COVID-19. The categories under Treatment and Preventive Measures indicated the enhanced self-care of individuals and also the tendencies to minimize the spread of diseases. The emergence of the theme Inclination toward Indigenous Knowledge, which is an important finding, indicated the techno-experts inclination toward the indigenous knowledge amid vague scientific shreds of evidence.

Comparative UAV Noise-Impact Assessments through Survey and Noise Measurements

Possibilities to use unmanned aerial vehicles (UAVs) are rapidly growing. With the development of battery technologies, communication, navigation, surveillance, and autonomous systems in general, many UAVs are expected to operate at relatively low altitudes. Thus, the problem of UAV noise impact on human health and well-being will be more pronounced. In this paper, we conducted noise measurements of two UAVs of different performance (quadrotor and hexarotor) in flying up and down, hovering, and overflight procedures. Respondents of good hearing who were confirmed by audiogram measurement and had participated in the survey during UAV noise measurement gave their subjective assessments on the UAV noise perception. UAV noise measurements and subjective respondents’ assessments were analysed and related. UAV noise analysis showed that the parameters measured at the same measurement point for the hexarotor were higher than those for the quadrotor in flying up and down and flying-over procedures. Low frequency noise was present in the noise spectrum of both drones. Participants were able to distinguish between the noise of UAVs and had a generally more negative experience with the hexarotor. Regardless of the noise perception, more than 80% of the respondents believe there are more pros than cons for UAV introduction into everyday life.

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

Drones as internet of video things front-end sensors: challenges and opportunities

Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.

Technological advancement in the era of COVID-19

Regional and local governments worldwide are working tirelessly toward effective ways of addressing the COVID-19 crisis. During this time, the government has had to ensure that they provide full usage of technological means to confront the pandemic and discourse a wide range of COVID-19 related problems. Herein, this article will discuss the application of technical means and the advancement of technology in different sectors as a consequence of the COVID-19 crisis. Further, it highlights how government and health organizations have introduced new policies intending to try to curb the spread of the coronavirus. These new policies, such as lockdowns and social distancing measures, have resulted in technological advancement and new means of interaction with government, businesses, and citizens. Such changes include increased online shopping, as well as robotic delivery systems, the introduction of digital as well as contactless payment systems, remote working, the role of technology in distance learning, Telehealth, 3D Printing, and online entertainment. These technological advancements have been embraced all the way during this pandemic by a few countries around the world, with its limitation in some underdeveloped and developing countries.

A Hybrid Approach to Solve the Vehicle Routing Problem with Time Windows and Synchronized Visits In-Home Health Care

With technological progress in particular telemedicine and health care, the information should meet and serve as well the needs of people and in particular whom with reduced mobility, the elderly as well as people with difficulties to access to medical resources and services. These services should be achieved in a fast and reliable manner based on case priorities. One of the major challenges in health care is the routing and scheduling problem to meet people's needs. Of course, the objective is to considerably minimize costs while respecting priorities according to cases that will face. Through this article, we propose a new technique for home healthcare routing and scheduling problem purely based on an artificial intelligence technique to optimize the offered services within a distributed environment. The automatic learning and search method seem to be interesting to optimize the allocation of visits to beneficiaries. The proposed approach has several advantages in terms of especially cost, efforts, and gaining time. A comparative study was carried out to evaluate the effectiveness of the planned technique compared to previous work.