A Drone-based Networked System and Methods for Combating Coronavirus Disease (COVID-19) Pandemic (2006.06943v2)

Abstract: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It is similar to influenza viruses and raises concerns through alarming levels of spread and severity resulting in an ongoing pandemic worldwide. Within eight months (by August 2020), it infected 24.0 million persons worldwide and over 824 thousand have died. Drones or Unmanned Aerial Vehicles (UAVs) are very helpful in handling the COVID-19 pandemic. This work investigates the drone-based systems, COVID-19 pandemic situations, and proposes an architecture for handling pandemic situations in different scenarios using real-time and simulation-based scenarios. The proposed architecture uses wearable sensors to record the observations in Body Area Networks (BANs) in a push-pull data fetching mechanism. The proposed architecture is found to be useful in remote and highly congested pandemic areas where either the wireless or Internet connectivity is a major issue or chances of COVID-19 spreading are high. It collects and stores the substantial amount of data in a stipulated period and helps to take appropriate action as and when required. In real-time drone-based healthcare system implementation for COVID-19 operations, it is observed that a large area can be covered for sanitization, thermal image collection, and patient identification within a short period (2 KMs within 10 minutes approx.) through aerial route. In the simulation, the same statistics are observed with an addition of collision-resistant strategies working successfully for indoor and outdoor healthcare operations. Further, open challenges are identified and promising research directions are highlighted.

Drone-Based Networked System for Combatting COVID-19: An Analytical Overview

The research paper discusses an innovative approach involving drone-based systems to address the challenges posed by the COVID-19 pandemic. The authors present a meticulously designed architecture that utilizes unmanned aerial vehicles (UAVs) for healthcare operations in areas where traditional connectivity and mobility are impeded. This paper not only elucidates the utility of drones in pandemic situations but also proposes advancements that leverage the Internet of Things (IoT), edge, fog, and cloud computing services to create a comprehensive smart healthcare system.

Summary of Proposed System

At its core, the proposed architecture integrates drones equipped with wearable sensors to compile data on health metrics and environmental conditions. It employs a push-pull data fetching mechanism to dynamically update and analyze information. The use of AI and machine learning processes within this architecture enhances the data collection, analysis, and subsequent decision-making procedures. This system supports both real-time monitoring in urban and simulated scenarios, effectively deploying drones for sanitization, thermal imaging, patient identification, and medication delivery tasks.

Simulation and Real-Time Implementation

The paper reports significant outcomes from deploying the proposed system. Real-time implementation in Delhi/NCR, India demonstrates the capability of drones to cover extensive areas expeditiously—sanitizing 2 KM within approximately 10 minutes. The simulation also supports these results, with the addition of collision-resistant capabilities for both indoor and outdoor operations, a crucial enhancement given the autonomy of drone systems. The authors highlight the success rate of these operations at about 95%, underscoring the feasibility and efficacy of the drone network in widespread pandemic scenarios.

Technical Considerations and Algorithms

The research presents various algorithmic strategies for drone navigation and collision avoidance, including single-layer zone-transfer algorithms and multi-layer strategies that provide flexibility and enhance operational effectiveness. A federated learning approach is employed, allowing drones to share learning experiences with edge and cloud servers, facilitating informed collective decision-making. The architecture also integrates several networking technologies such as IoMT and IoD to enhance data handling and integration.

Implications and Future Directions

The implications of this research are manifold. Practically, the proposed system can significantly augment current healthcare responses to pandemics, especially in regions where resource access is limited, or urban density complicates traditional healthcare delivery. Theoretically, the integration of AI in drone-based systems offers a frontier for further exploration in autonomous healthcare services.

The authors identify key challenges and future research directions, notably the need for:

1. Large-scale medicine delivery systems tailored to drone-based logistics.
2. Comprehensive scanning in environments with infrastructure constraints.
3. Comparative efficacy analyses between traditional and drone-based health services.
4. Enhanced mini-drone designs for intricate indoor operations.

Conclusion

This research contributes to the growing field of drone-based healthcare interventions by providing a robust framework for its application during pandemics like COVID-19. The proposed system's success in real-world trials and simulations reflects the potential for substantial improvements in remote healthcare delivery. As drone technology and AI continue to evolve, such integrated systems could revolutionize emergency and routine healthcare processes worldwide. The paper wisely emphasizes a systematic deployment of drones not only for immediate pandemic responses but as a critical component of future public health strategies.