Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning (1609.05362v3)

Abstract: Unmanned Aerial Vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV that take place by means of frequency division duplex (FDD) via orthogonal or non-orthogonal multiple access (NOMA) schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communication as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation (SCA) strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory.

• The paper introduces a joint optimization framework that integrates bit allocation for uplink, computation, and downlink with UAV trajectory planning using successive convex approximation.
• The paper demonstrates substantial energy savings over non-optimized methods through numerical results under stringent latency and energy constraints.
• The paper highlights practical applications for mobile edge computing in infrastructure-limited scenarios and suggests future work on dynamic user mobility and real-time optimization.

Optimization of Bit Allocation and Path Planning in UAV-Mounted Cloudlet Systems

The paper explores a UAV-enabled mobile cloud computing architecture that leverages unmanned aerial vehicles (UAVs) as cloudlets to facilitate computation offloading for mobile users (MUs) with constrained local processing capabilities. The primary objective is to optimize the bit allocation for uplink, downlink, and cloudlet computing alongside the UAV's trajectory to minimize mobile energy consumption, while adhering to quality of service (QoS) requirements.

Overview of the System Model

In this model, a UAV acts as a flying cloudlet providing offloading opportunities to MUs. The system is divided into three phases:

1. Uplink Transmission: MUs transmit application input data to the UAV.
2. Cloudlet Computation: The UAV processes the data.
3. Downlink Transmission: Results are sent back to the MUs.

The paper considers orthogonal and non-orthogonal multiple access (NOMA) schemes for communication between the UAV and MUs.

Problem Formulation

The authors frame the optimization as a joint problem of minimizing the total energy consumption of MUs. The variables include bit allocation for uplink and downlink, computing at the cloudlet, and the UAV's trajectory, subject to constraints on latency and the UAV's energy budget. The UAV’s trajectory is optimized, considering two flying energy models—one dependent solely on velocity and another incorporating both velocity and acceleration.

Methodology

The optimization is approached using successive convex approximation (SCA) strategies, which involve creating convex approximations of the non-convex problem and iteratively solving it to converge to a local minimum. The SCA framework efficiently addresses the joint optimization of communication, computation, and path planning.

Numerical Results and Analysis

Numerical evaluations demonstrate that the proposed joint optimization approach significantly reduces the mobile energy consumption compared to non-optimized methods and partial optimizations such as fixed bit allocation or constant-velocity UAV trajectories. Notably, when the reference SNR and latency constraints are sufficiently managed, the offloading shows considerable energy savings compared to full local execution.

Implications and Future Work

The research offers substantial implications for mobile edge computing in scenarios lacking robust infrastructure, such as disaster zones or rural areas. The UAV-based cloudlet system, by optimizing resource deployment and energy consumption, becomes a promising solution for extending computational services.

Looking forward, further exploration could be directed towards applications involving dynamically moving MUs and unpredictable network conditions. Additionally, integrating machine learning techniques for real-time optimization could enhance system adaptability.

This comprehensive paper enriches the field by presenting a robust methodology to optimize cloudlet-assisted offloading for UAV systems, providing a foundational step towards more efficient mobile cloud architectures.