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

TL;DR

This paper presents a drone-based system architecture utilizing wearable sensors and real-time data collection to enhance COVID-19 pandemic response, especially in congested or connectivity-challenged areas, demonstrating effective sanitization and patient monitoring.

Contribution

It proposes a novel drone-based architecture integrating wearable sensors and simulation strategies for pandemic management in diverse scenarios.

Findings

Large area coverage for sanitization and monitoring within 10 minutes.

Effective data collection in remote and congested areas with connectivity issues.

Collision-resistant strategies improve indoor and outdoor healthcare operations.

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.