Version 1
: Received: 24 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (04:12:51 CET)
How to cite:
Roy, A.; Imandi, R.; B N, P.K. FU-Serve: Fog-enabled UAV-as-a-Service for IoT Applications. Preprints2023, 2023111757. https://doi.org/10.20944/preprints202311.1757.v1
Roy, A.; Imandi, R.; B N, P.K. FU-Serve: Fog-enabled UAV-as-a-Service for IoT Applications. Preprints 2023, 2023111757. https://doi.org/10.20944/preprints202311.1757.v1
Roy, A.; Imandi, R.; B N, P.K. FU-Serve: Fog-enabled UAV-as-a-Service for IoT Applications. Preprints2023, 2023111757. https://doi.org/10.20944/preprints202311.1757.v1
APA Style
Roy, A., Imandi, R., & B N, P.K. (2023). FU-Serve: Fog-enabled UAV-as-a-Service for IoT Applications. Preprints. https://doi.org/10.20944/preprints202311.1757.v1
Chicago/Turabian Style
Roy, A., Raju Imandi and Pavan Kumar B N. 2023 "FU-Serve: Fog-enabled UAV-as-a-Service for IoT Applications" Preprints. https://doi.org/10.20944/preprints202311.1757.v1
Abstract
In this work, we propose a Fog-enabled UAV-as-a-Service (FU-Serve) architecture to address the issues of data transmission delay for serving time-critical Internet of Things (IoT) applications. Traditionally, in a UAV-as-a-Service (UaaS) platform, different UAVs host heterogeneous sensors, which sense the physical phenomenon and transmit the sensed data to a centralized entity. Transmission of data from the sensors to the centralized entity and making any decision for an application consumes a significant amount of time. Consequently, the traditional UaaS architecture is unsuitable for serving time-critical IoT applications such as transportation, healthcare, and industries. To address these issues of service latency for time-critical IoT applications, we present the FU-Serve architecture by introducing the concept of fog computing in the UaaS platform. We discuss all the components of FU-Serve elaborately in this paper. Additionally, we architect optimal and dynamic fog node selection mechanisms for FU-Serve, which reduce the transmission delay in the networks. The simulation results show that the FU-Serve outperforms by $75\%$ compared to the traditional UaaS platform.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
