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The outbreak of COVID-19 has proved that the world needs to be more prepared for pandemic situations. In response to the COVID-19 pandemic, a rise in innovation, automation, and smart Internet of Things (IoT) solutions has occurred. Smart IoT solutions are of significant importance in monitoring, contact tracing, and diagnosing/detecting COVID-19 patients. These solutions have also become vital in the continuance of regular life, work, education, and social events that would otherwise not be possible for many during quarantine conditions. Additionally, life has become challenging during COVID-19 for people in rural and remote communities with limited wireless connectivity. Unmanned aerial vehicle (UAV)-assisted networks can provide wireless connectivity and, in combination with smart IoT solutions, are essential to combat COVID-19. Driven by this, we provide a review of UAV-assisted IoT networks and propose a scheduling scheme for on-demand sensing to manage the pandemic's impact. First, we discuss applications of UAV-assisted IoT networks around the world and different scenarios for utilization managing the impact of the pandemic. We then provide an overview of recent studies on UAV-assisted IoT networks for the pandemic. We then present the proposed scheduling scheme for on-demand sensing using UAV-assisted IoT networks. Next, we provide the various challenges facing the widespread utilization of UAV-assisted IoT networks. Lastly, we present possible directions for future research on this topic.

Unmanned aerial vehicles (UAVs) base stations are a promising solution to provide better coverage to ground users. In this paper, balloons, UAVs and users are deployed to efficiently enhance users’ coverage. This paper studies three deployment problems: (i) placement of balloons to aid UAVs for wireless assistance, (ii) placement of UAVs to provide coverage to users, and (iii) a joint problem for successful deployment of balloons, UAVs, and connectivity to users. Firstly, an optimization problem is formulated to minimize the number and costs of balloons. Secondly, the goal is to minimize the number of UAVs, maximize the number of users’ connections, minimize the distance between users and UAVs, and minimize the UAVs’ deployment costs. Thirdly, a joint problem is also introduced to provide efficient wireless coverage. The problem is formulated as an integer linear program (ILP). An optimal algorithm is used to solve the optimization problem, however, the computational complexity of an optimal algorithm is high. Thus, a low-complexity greedy algorithm is also proposed. Simulation results show the efficiency of the proposed optimal and greedy algorithms.