A drone-based networked system and methods for combating coronavirus disease (covid-19) pandemic
Open accessAbstract
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 min 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.
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In drone scenarios, drones can make smarter decisions, such as whether to send all data to the cloud or process it onboard.
“In this scenario, drones themselves can make smarter decisions, for example, whether it sends the whole data to the cloud or completes the data processing on board [155].”
From Edge AI: A survey (2023) · cites this paper as [155]
The “Drone-System” and “Edge Network and Computing System” passages in “A drone-based architecture for smart healthcare system” describe initial processing on the drone and sending necessary data onward for further processing. They do not describe a drone choosing between sending all data to the cloud and completing all processing onboard; the local-versus-remote decision is assigned to the edge system. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredInherent problems of drones are significant challenges such as limited flight duration due to short battery lifetime, limited CPU resource and memory, and security concerns.
“Despite the beneficial use of drones in smart cities, the inherent problems of drones are also significant challenges such as limited flight duration due to short battery lifetime, limited CPU resource and memory, and security concerns [7,155,156].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [156]
In Performance evaluation, Case 1 (page unavailable), Table 3 reports flight times of 12–15 or 35–40 minutes for the paper’s drone; Case 2 also notes that recharging time is additional. Open challenges and future directions calls for security protocols and further security analysis. These passages support flight-duration and security concerns, but do not establish short battery lifetime as the cause or identify limited onboard CPU and memory as challenges. (LLM, conf=0.83)
Experimental — accuracy not yet measuredChina has recently exploited the mobility of drones to improve the provisions of medical supplies.
“More interesting, China has recently exploited the mobility of drones [173] to improve the provisions of medical supplies [174].”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [174]
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“Despite the beneficial use of drones in smart cities, the inherent problems of drones are also significant challenges such as limited flight duration due to short battery lifetime, limited CPU resource and memory, and security concerns [7,155,156].”
Background
- Authors
- Kumar, A., Sharma, K., Singh, H., Naugriya, S. G., Gill, S. S., & Buyya, R.
- Affiliations
- University of Petroleum and Energy Studies (IN)
- Queen Mary University of London (GB)
- The University of Melbourne (AU)
- Corresponding author
- Kumar, Adarsh
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 115
- Pages
- 1-19
- ISSN
- 0167-739X (ISSN-L)
- PMC
- PMC7467876
- OpenAlex
- W3035681721
- Cited by
- 252
- References
- 32
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 20 · 2023: 43 · 2022: 76 · 2021: 79 · 2020: 16
- FWCI
- 32.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Future Generation Computer Systems (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- COVID-19 pandemic, unmanned aerial vehicles, body area networks, wearable sensors, remote care, infrared thermography, sanitization, patient identification, collision avoidance, wireless connectivity, data collection architecture
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