Dynamic Computation Offloading Based on Q-Learning for UAV-Based Mobile Edge Computing

• 영문명: Dynamic Computation Offloading Based on Q-Learning for UAV-Based Mobile Edge Computing
• 발행기관: 한국스마트미디어학회
• 저자명: Shreya Khisa Sangman Moh
• 간행물 정보: 『스마트미디어저널』Vol12, No.3, 68~76쪽, 전체 9쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2023.04.30

영문 초록

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.

목차

Ⅰ. INTRODUCTION

Ⅱ. RELATED WORKS

Ⅲ. SYSTEM MODEL

Ⅳ. Q-LEATNING-BASED COMPUTTAION OFFLOADING

Ⅴ. PERFORMANCE EVALUATION

Ⅵ. CONLCUSION

키워드

Computation offloading energy efficiency mobile edge computing Q-learning reinforcement learning