移动边缘计算
计算机科学
正确性
强化学习
边缘计算
最优化问题
GSM演进的增强数据速率
云计算
数学优化
物联网
整数(计算机科学)
能源消耗
任务(项目管理)
分布式计算
人工智能
工程类
算法
嵌入式系统
数学
程序设计语言
系统工程
电气工程
操作系统
作者
Tian Kang,Haojun Chai,Yameng Liu,Boyang Liu
出处
期刊:Electronics
[MDPI AG]
日期:2022-03-10
卷期号:11 (6): 879-879
被引量:9
标识
DOI:10.3390/electronics11060879
摘要
Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm.
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