A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs
This addresses efficiency and scalability issues for UAV-based large-scale missions like inspections, but it is incremental as it builds on existing fog-computing architectures.
The paper tackles the problem of limited information exchange in UAV operations due to distance, bandwidth, and processing constraints by proposing a mathematical model and fog-cloud computing framework, with tests successfully predicting latency and operational constraints to analyze fog-computing advantages.
Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.