Fairness-Aware Task Loss Rate Minimization for Multi-UAV Enabled Mobile Edge Computing
Chong Zhu, Guopeng Zhang, Kun Yang
Abstract
In practical systems, a computing task generated by an Internet of Things device (IoTD) is usually given a valid period ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vap</monospace> ). The tasks that cannot be executed within the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vap</monospace> will be dropped. The main goal of this letter is to minimize the task loss rate ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TLR</monospace> ) in unmanned-air-vehicle (UAV) assisted mobile edge computing (MEC) due to the timeout. Furthermore, to ensure an equal service opportunity for the IoTDs and an equal energy consumption ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EC</monospace> ) level for the UAVs, the issues of the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TLR</monospace> -fairness between IoTDs and the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EC</monospace> -fairness between UAVs are also considered. This is formulated as the mixed integer nonlinear programming (MINLP), which is difficult to be addressed by traditional methods, especially when the fast decision-making process is required. To address this problem, we present a new solution based on the multi-agent deep deterministic policy gradient (MA-DDPG) to optimize the flight trajectory, the association between the UAVs and IoTDs and the task scheduling of the IoTDs. Simulation results verify the effectiveness of the proposed MA-DDPG based algorithm.