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Building of Drone-Based Next Generation Edge Computing Model: With Efficient Resource Allocation and Secured 5G-and-beyond Networks
Priyan Malarvizhi Kumar, Tayyaba Shahwar and Gokulnath C

The ERAS5G model would present an innovative approach combining edge computing via drones with efficient resource allocation and secure 5G networks. Using portable terminals with low battery life by using Mobile Edge Computing (MEC) servers and energy transmitters in UAVs would be an opportunity. Wireless computing capabilities and energy harvesting using RF energy from UAVs to mobile terminals will be highlighted in the report. According to the abstract, Frequency-Division Multiple Access (FDMA) protocols will improve communication between drones and devices with the same frequency band. Using a hybrid Linear Discriminant Analysis Logistic Regression (LDA-LR) algorithm in the model would emphasize the importance of considering strong security measures when securing 5G networks. Using the same strategy, the abstract discusses computing collaboration between devices and edge servers to optimize task execution and computation. This is related to resource allocation. A focus would be on utilizing energy-efficient methods and allocating computing resources dynamically. The ERAS5G model, with particular attention to its technological advancements in UAV-based MEC networks, 5G communications security, and energy conservation. The model’s potential impact on wireless communication and advanced computing technologies. The parameters are calculated and are compared, and analyzed, completed task Vs UAV SPU frequency, completed task Vs UAV transmit power, energy consumption, average consumption, average energy consumption, network accuracy, UAV’s speed, UAV’s acceleration are defined and explained how our ERAS5G Model is more efficient, increased network stability when compared to other existing models.

Keywords: Mobile Edge Computing (MEC), Frequency-Division Multiple Access (FDMA), Linear Discriminant Analysis (LDA), Logistic Regression (LR)