Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2007.15105v1)

Abstract: In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.

• The paper introduces a novel optimization model that integrates UAV trajectory design with resource allocation to maximize energy efficiency.
• It employs the Dinkelbach algorithm, successive convex approximation, and ADMM for distributed and scalable problem-solving.
• Simulation results demonstrate significant improvements over baseline designs, informing advanced UAV-assisted MEC deployments.

Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

This paper addresses the challenge of optimizing energy efficiency in Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems. The authors focus on improving computation offloading with minimal energy consumption by the UAV, which acts as an aerial cloudlet for ground users' computational tasks. The optimization targets the UAV's trajectory, user transmit power, and computation load allocation, leading to a complex nonconvex fractional problem.

Key Contributions

1. Optimization Model: The paper presents a model that integrates UAV trajectory design with resource allocation in MEC systems, considering UAVs' mechanical constraints and users' communication and computational needs. Using the Dinkelbach algorithm and the successive convex approximation (SCA) technique helps transform the nonconvex problem into a solvable form, enabling distributed and scalable problem-solving.
2. Energy Efficiency Focus: By defining energy efficiency as the ratio of total offloaded data to UAV energy consumption, the paper provides a nuanced approach to resource management that considers both communication and computational workload in MEC settings.
3. Spatial Distribution Estimation: To tackle the uncertainty in user mobility, the authors employ Gaussian kernel density estimation to predict user locations. This approach allows for proactive UAV trajectory planning when initial user mobility data is limited.
4. Distributed Optimization: The problem is decomposed into subproblems solvable via the Alternating Direction Method of Multipliers (ADMM), with UAVs and ground users iterating cooperatively. This method enhances scalability and computational efficiency by localizing decision-making and minimizing information exchange.

Numerical Results and Implications

Simulation results verify the proposed approach's effectiveness in maximizing energy efficiency, demonstrating significant improvements over baseline trajectory designs. The non-orthogonal channel access scheme generally outperforms orthogonal models due to better channel capacity, thus reducing energy usage by the UAV.

Implications and Future Directions

The integration of UAVs in MEC for IoT deployments opens pathways for deploying computational services in regions without ground infrastructure. Practically, this means extending intelligent computing services to remote and dynamic environments, enhancing the utility of IoT nodes significantly.

The paper provides a foundation for further niche explorations in MEC, such as refining dynamic UAV deployment strategies or considering broader environmental constraints. Further, incorporating advanced machine learning techniques for real-time adaptation and prediction in volatile MEC environments marks a promising area for future research.

Theoretical Impacts

The proposed solutions have implications for theoretical advancements in optimization under constraints typical to wireless network operations. Exploring convex approximations and distributed problem solutions introduces potential new methodologies for tackling complex optimization problems in other energy-constrained domains.

This work effectively navigates the complexities of energy-efficient MEC involving UAVs, yielding practical insights and theoretical frameworks that merit further exploration and application in the rapidly evolving field of mobile edge services.