An Edge-Cloud Approach for Dynamic Resource Allocation in Drone Communication Networks

The increasing use of drones in diverse sectors such as surveillance, disaster response, and delivery services emphasizes the crucial need for reliable and seamless communication. Centralized cloud computing models face challenges in meeting the dynamic operational needs and low-latency requirements of drones. Therefore, an innovative solution is essential to address these constraints. This paper addresses the critical challenge of dynamic resource allocation in drone communication networks to enhance efficiency in real-time applications. Our proposed approach uses the synergies between edge and cloud computing, introducing a novel framework that dynamically allocates resources based on the specific needs of drone communication networks. The model considers factors such as drone mobility, varying communication loads, and latency constraints to make intelligent resource allocation decisions at the network's edge. The proposed work aims to enhance the resource utilization efficiency of drone communication networks through intelligent and adaptive resource allocation strategies. The proposed model showcases a notable reduction in latency, improved scalability, and efficient utilization of resources. The proposed edge-cloud approach also provides a scalable and adaptive communication infrastructure for improving the overall performance of drone networks.