Attention-augmented multi-agent collaboration for Smart Industrial Internet of Things task offloading

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yihang Wang , Shengchao Su , Yiwang Wang
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Abstract

The integration of Multi-access Edge Computing (MEC) technology within the Smart Industrial Internet of Things (SIIoT) ecosystem can significantly enhance both computational and storage capabilities. This advancement facilitates improved data processing and a more efficient utilization of resources in industrial applications. However, the high density of devices typical of SIIoT environments often presents several challenges, including a low success rate for task offloading, increased latency, higher energy consumption, and the risk of overloading edge servers. This paper addresses these challenges by treating Smart Devices (SDs) as agents and proposing a collaborative multi-agent task offloading strategy. A computational offloading model has been developed to minimize delayed energy consumption, which is then formulated as a Multi-Agent Partially Observable Markov Decision Process (MAPOMDP) featuring a hybrid action space composed of discrete and continuous elements. An attention mechanism is introduced to tackle the complex competition for edge server resources among SDs during the offloading process, enabling the observation of the actions and states of other devices within the system. A Prioritized Experience Replay (PER) mechanism is employed to optimize the training process. A Multi-Agent Attention Deep Reinforcement Learning (MA2DRL) algorithm is proposed to improve computational task offloading. Experimental results demonstrate that the proposed algorithm outperforms other comparative algorithms regarding task offloading latency, average energy consumption, offloading success rate, and server load variance.

Abstract Image

用于智能工业物联网任务卸载的注意力增强型多代理协作
在智能工业物联网(SIIoT)生态系统中集成多访问边缘计算(MEC)技术,可显著提高计算和存储能力。这一进步有助于改进数据处理,更有效地利用工业应用中的资源。然而,SIIoT 环境中典型的高密度设备往往会带来一些挑战,包括任务卸载成功率低、延迟增加、能耗增加以及边缘服务器超载的风险。本文将智能设备(SD)视为代理,并提出了一种协作式多代理任务卸载策略,以应对这些挑战。为了最小化延迟能耗,本文开发了一种计算卸载模型,并将其表述为多代理部分可观测马尔可夫决策过程(MAPOMDP),其特点是由离散和连续元素组成的混合行动空间。在卸载过程中,为解决 SD 之间争夺边缘服务器资源的复杂竞争问题,引入了一种关注机制,使观测系统内其他设备的行动和状态成为可能。采用优先经验重放(PER)机制来优化训练过程。为改进计算任务卸载,提出了一种多代理注意深度强化学习(MA2DRL)算法。实验结果表明,在任务卸载延迟、平均能耗、卸载成功率和服务器负载差异方面,所提出的算法优于其他比较算法。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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