Real-Time Reliability Monitoring on Edge Computing: a Systematic Mapping

Mario José Diván, Dmitry Shchemelinin, Marcos E. Carranza, Cesar Ignacio Martinez-Spessot, Mikhail Buinevich
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Abstract

Scenario: System reliability monitoring focuses on determining the level at which the system works as expected (under certain conditions and over time) based on requirements. The edge computing environment is heterogeneous and distributed. It may lack central control due to the scope, number, and volume of stakeholders. Objective: To identify and characterize the Real-time System Reliability Monitoring strategies that have considered Artificial Intelligence models for supporting decision-making processes. Methodology: An analysis based on the Systematic Mapping Study was performed on December 14, 2022. The IEEE and Scopus databases were considered in the exploration. Results: 50 articles addressing the subject between 2013 and 2022 with growing interest. The core use of this technology is related to networking and health areas, articulating Body sensor networks or data policies management (collecting, routing, transmission, and workload management) with edge computing. Conclusions: Real-time Reliability Monitoring in edge computing is ongoing and still nascent. It lacks standards but has taken importance and interest in the last two years. Most articles focused on Push-based data collection methods for supporting centralized decision-making strategies. Additionally, to networking and health, it concentrated and deployed on industrial and environmental monitoring. However, there are multiple opportunities and paths to walk to improve it. E.g., data interoperability, federated and collaborative decision-making models, formalization of the experimental design for measurement process, data sovereignty, organizational memory to capitalize previous knowledge (and experiences), calibration and recalibration strategies for data sources.
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基于边缘计算的实时可靠性监测:一种系统映射
场景:系统可靠性监控侧重于根据需求确定系统按预期(在特定条件下和随时间推移)工作的级别。边缘计算环境是异构的、分布式的。由于涉众的范围、数量和数量,它可能缺乏集中控制。目的:识别和表征考虑人工智能模型支持决策过程的实时系统可靠性监测策略。方法:分析基于系统测绘研究于2022年12月14日进行。在探索中考虑了IEEE和Scopus数据库。结果:在2013年至2022年期间,有50篇文章讨论了这一主题,并引起了越来越多的兴趣。该技术的核心用途与网络和健康领域有关,将身体传感器网络或数据策略管理(收集、路由、传输和工作负载管理)与边缘计算结合起来。结论:边缘计算的实时可靠性监测正在进行中,仍处于初期阶段。它缺乏标准,但在过去两年中已引起重视和兴趣。大多数文章关注的是支持集中式决策策略的基于推送的数据收集方法。此外,除了网络和健康之外,它还集中并部署在工业和环境监测方面。然而,有很多机会和途径可以改善它。例如,数据互操作性,联合和协作决策模型,测量过程实验设计的形式化,数据主权,利用以前的知识(和经验)的组织记忆,数据源的校准和重新校准策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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