Joint optimization of system utility in UAV-enabled edge computing.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Mobile Edge Computing (MEC) networks are increasingly integrated with Unmanned Aerial Vehicles (UAVs).
• Traditional optimization methods often focus on individual network components, leading to suboptimal overall system performance.
• Maximizing system utility, defined as the balance between service provider revenue and user costs, is crucial for efficient UAV-enabled MEC networks.

Purpose of the Study:

• To develop a novel framework for the joint optimization of system utility in UAV-enabled MEC networks.
• To address realistic constraints such as limited computational resources and energy consumption.
• To propose an efficient algorithm for solving the complex optimization problem.

Main Methods:

• Formulation of the joint optimization problem as a Mixed-Integer Nonlinear Programming (MINLP) model.
• Development of an efficient algorithm integrating the Block Successive Upper-Bound Minimization (BSUM) framework with heuristic methods.
• Decomposition of the MINLP problem into tractable subproblems solved iteratively.

Main Results:

• The proposed joint optimization approach significantly outperforms traditional heuristic algorithms in maximizing system utility.
• The algorithm demonstrates robust convergence and reliability across diverse network configurations.
• The study validates the effectiveness of joint optimization in enhancing both economic and operational efficiency.

Conclusions:

• Joint optimization is key to improving the overall system utility in UAV-enabled MEC networks.
• The developed algorithm provides an efficient and reliable solution for complex optimization challenges.
• This research lays the groundwork for future advancements in network utility management for UAV-assisted systems.