A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

TL;DR

This paper proposes a fog-cloud computing framework and mathematical model to enhance UAV information processing, addressing limitations like bandwidth and energy constraints, and demonstrating improved latency and scalability for large-scale operations.

Contribution

It introduces a novel fog-cloud computing framework and a mathematical model for analyzing UAV topologies, improving large-scale mission efficiency.

Findings

Predicted latency and operational constraints successfully.

Demonstrated advantages of fog computing over traditional cloud architectures.

Enhanced scalability and efficiency for UAV operations.

Abstract

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.