Unmanned Aerial Vehicles by Implementing Mobile Edge Computing for Resource Allocation

Unmanned Aerial Vehicles (UAVs) have become indispensable across various sectors, spanning from environmental surveillance and agricultural monitoring to last-mile package delivery services. The efficiency and effectiveness of UAV operations heavily rely on robust resource allocation strategies. Recognizing this need, Mobile Edge Computing (MEC) has been a absolute solution. MEC offers computational and storage resources, bringing them closer to where UAVs operate. This proximity minimizes latency and enhances data processing capabilities, thus significantly improving UAV performance. In this research, we delve into the potential of MEC in optimizing resource allocation for UAVs. Our proposed framework capitalizes on the Preferential Cyclic Reassignment Mechanism (PCRAM), a sophisticated mechanism within an MEC-enabled architecture.By integrating PCRAM with MEC infrastructure, we aim to streamline resource allocation processes for UAVs. This approach ensures that computational tasks and data storage are allocated efficiently, maximizing UAV performance while minimizing resource wastage and latency. Through this investigation, we seek to demonstrate the viability and efficacy of leveraging MEC for resource allocation in UAV operations. Ultimately, our research aims to contribute to the advancement of UAV technology by enhancing their capabilities through intelligent resource management mechanisms.