Secrecy Energy Efficiency Enhancement in UAV-Assisted MEC System

Area of Science:

• * Wireless Communication and Networking
• * Mobile Edge Computing (MEC)
• * Internet-of-Things (IoT) Security

Background:

• * Addressing computational power and security challenges in IoT environments.
• * Leveraging Unmanned Aerial Vehicles (UAVs) for enhanced mobile edge computing capabilities.
• * The need for robust security and energy efficiency in UAV-assisted MEC systems.

Purpose of the Study:

• * To propose a secrecy energy efficiency optimization scheme for multifunctional UAV-assisted MEC systems.
• * To enhance the security and energy efficiency of task offloading in IoT scenarios.
• * To jointly optimize UAV trajectory, power, user scheduling, and task offloading.

Main Methods:

• * Development of a multifunctional UAV capable of switching roles (computing or jamming).
• * Joint optimization of UAV trajectory, uplink transmit power, user scheduling, and offload task.
• * Application of an updated-rate assisted block coordinate descent (BCD) algorithm.

Main Results:

• * The proposed scheme effectively enhances system secrecy performance and energy efficiency.
• * Simulation results demonstrate significant improvements compared to benchmark schemes.
• * A notable 38.5% improvement in secrecy energy efficiency was achieved.

Conclusions:

• * The developed optimization scheme provides a viable solution for secure and energy-efficient UAV-assisted MEC in IoT.
• * The flexible role-switching capability of the UAV contributes to improved system performance.
• * The joint optimization approach effectively balances security and energy efficiency objectives.