Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.
Sensors (Basel)
; 24(13)2024 Jul 04.
Article
in En
| MEDLINE
| ID: mdl-39001116
ABSTRACT
This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.
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Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Sensors (Basel)
Year:
2024
Document type:
Article
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