Dynamic Models and Control Techniques for Drone Delivery of Medications and Other Healthcare Items in COVID-19 Hotspots

• Published in: Emerging Technologies for Battling Covid-19 Vol. 324; pp. 1 - 34
• Main Authors: Sharma, Kriti, Singh, Harvinder, Sharma, Deepak Kumar, Kumar, Adarsh, Nayyar, Anand, Krishnamurthi, Rajalakshmi
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Studies in Systems, Decision and Control
• Subjects: COVID-19; Dynamic models; Healthcare 4.0; Industry 4.0; Pandemic; Simulation
• ISBN: 9783030600389; 3030600386
• ISSN: 2198-4182; 2198-4190
• DOI: 10.1007/978-3-030-60039-6_1

Drone-based dynamic model and control techniques vary from classical linear proportional integral derivative (CPID) to complex nonlinear multiconstrained and multi-objective schemes such as backstepping, sliding window mode, size-based models, and operation-based models, among others. These approaches can be classified as per their usage. Thus, some will be efficient for indoor operations, and others will be useful for outdoor operations. The performance of both types of drone-based smart healthcare systems can be measured in terms of stabilizing the attitude for both indoor and outdoor operations as per requirements. Further, gain-based drone scheduling is commonly used in flight controllers. In COVID-19 pandemic situations, the gains can be measured using an alternative way. Here, different parameters like medication advantage to COVID-19 pandemic areas, identifying the COVID-19 hotspots, sanitizing requirements and potentials, finding the COVID-19 chain, etc., could be considered in gain measurement for the deployment of drone-based COVID-19’s smart healthcare. This work proposes a multi-constraint and multi-objective gain-based simulation-optimization approach for scheduling the linear and nonlinear dynamic and controllable drone movement models. The proposed model considered the identity-based lower and upper limits of control interface. Further, this interface is having the provision to include some human factors in its execution. The performance of the overall system is measured using performance and security metrics. In performance, drone-based smart healthcare systems’ efficiency, accuracy, and effectiveness are measured. The measurements are analyzed by varying optimization parameters. In the security, lightweight cryptography primitives and protocols are analyzed for performance measurements. These lightweight cryptography primitives and protocols ensure secure data storage, transmission, and processing at any device. Further, the scope of centralized and distributed systems of drone cooperations for COVID-19 monitoring, sanitization, cleaning, and control room will be explored to have time-saving and autonomous drone-based smart healthcare systems.