Fairness-Aware Task Scheduling and Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, the limited battery capacity of UAV constrains its endurance time, and makes energy efficiency one of the top priorities in implementing UAV-enabled MEC architecture. In this backdrop, we aim to minimize the UAV's energy consumption by jointly optimizing its trajectory and resource allocation, and task decision and bits scheduling of users considering fairness. The problem is formulated as a mix-integer nonlinear programming problem with strongly coupled variants, and further transformed into three more tractable subproblems: 1) trajectory optimization PT; 2) task decision and bits scheduling PS; and 3) resource allocation PR. Then, we propose an iterative algorithm to deal with them in a sequence, and further design a penalty method-based algorithm to reduce computation complexity when the branch-and-bound (B&B) algorithm incurs a high complexity to solve PS. Simulation results demonstrate that our proposed algorithm can efficiently reduce the energy consumption of UAV, and help save 17.7% - 54.6% and 78.9% - 91.9% energy compared with Equal Resource Allocation and Random Resource Allocation. Moreover, it reduces more than 88% running time and achieves relatively satisfactory performance compared with B&B.

Automated summary

This paper focuses on the energy efficiency of drones in mobile edge computing, aiming to minimize drone energy consumption by optimizing trajectory, resource allocation, task decision, and user bit scheduling. Proposed iterative algorithm and penalty method-based algorithm to tackle the problem, comparing energy consumption with different resource allocations.