5G Technology based Edge Computing in UAV Networks for Resource Allocation with Routing using Federated Learning Access Network and Trajectory Routing Protocol
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Abstract
UAVs (Unmanned aerial vehicles) are being utilised more frequently in wireless communication networks of the Beyond Fifth Generation (B5G) that are equipped with a high-computation paradigm and intelligent applications. Due to the growing number of IoT (Internet of Things) devices in smart environments, these networks have the potential to produce a sizeable volume of heterogeneous data.This research propose novel technique in UAV based edge computing resource allocation and routing by machine learning technique. here the UAV-enabled MEC method regarding emerging IoT applications as well as role of machine learning (ML) has been analysed. In this research the UAV assisted edge computing resource allocation has been carried out using Monte Carlo federated learning based access network. Then the routing through UAV network has been carried out using trajectory based deterministic reinforcement collaborative routing protocol.We specifically conduct an experimental investigation of the tradeoff between the communication cost and the computation of the two possible methodologies.The key findings show that, despite the longer connection latency, the computation offloading strategy enables us to give a significantly greater throughput than the edge computing approach.
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