摘要：In Industrial Internet of Things (IIoT), a large volume of data is collected periodically by IoT devices, and timely data routing and processing are important requirements. Age of Information (AoI), which is a metric to evaluate the freshness of status information in data processing, has become one of the most important objectives in IIoT. In this paper, considering limited communication, computation and energy resources on IoT devices, we jointly study the optimal AoI-aware energy control and computation offloading problem within a dynamic IIoT scenario with multiple IoT devices and multiple edge servers. Based on extensive analysis of real-life IoT dataset, Markovian queueing models are constructed to capture the dynamics of IoT devices and edge servers, and their corresponding analyses are provided. With the quantitative analytical results, we formulate a dynamic Markov decision problem with the objective of minimizing the long-term energy consumption while satisfying AoI constraints for real-time data processing. To solve the problem, we apply Deep Reinforcement Learning (DRL) techniques for adapting to large-scale dynamic IIoT environments, and design an intelligent Energy Control and Computation Offloading (ECCO) algorithm. Extensive simulation experiments are conducted based on real-world dataset, and the comparison results illustrate the superiority of our ECCO algorithm over both existing DRL and non-DRL algorithms.(c) 2022 Elsevier B.V. All rights reserved.

关键字：Age of Information (AoI); Computation offloading; Deep Reinforcement Learning (DRL); Industrial Internet of Things (IIoT)

ISSN号：0167-739X

卷、期、页：卷: 139页: 29-37

发表日期：2023-02-01

影响因子：7.187000

期刊分区（SCI为中科院分区）：二区

收录情况：SCIE（科学引文索引网络版）

发表期刊名称：FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

参与作者：万少华,陈莹

通讯作者：高涵

第一作者：黄霁崴

论文类型：期刊论文

论文概要：黄霁崴,高涵,万少华,陈莹，AoI-aware energy control and computation offloading for industrial IoT，FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE，2023，卷: 139页: 29-37

论文题目：AoI-aware energy control and computation offloading for industrial IoT