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首页> 外文期刊>Future generation computer systems >Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments
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Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments

机译：自我发展的智能算法，可促进物联网环境中的数据互操作性

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摘要

Internet of Things (IoT) is predicted to connect 20.4 billion devices in 2020 and surge to 75 billion by 2025. Such a connected world where machines will communicate with other machines opens up huge opportunities and a very different way of life, with smart homes, self-driving vehicles and wearable devices. It is expected that such interconnectedness will enable the capture of events as data in real time and provide actionable insights to people and organizations to maximize efficiencies, be pro-active and more effective. Interconnected devices will require interoperability, and the seamless, secure and controlled exchange of data between devices and applications has been called data interoperability. Such a dynamic and volatile environment with a wide diversity of data will require a new breed of intelligent algorithms with the ability to adapt and self-learn as well as envisage and analyse events at multiple levels of abstraction to gauge association and interrelationships. This research proposes three algorithmic requirements for intelligent algorithms in such IoT environments: unsupervised self-learning capability, ability to self-generate to the environment and incrementally learn with temporal changes. The paper first presents empirical results with real data from a fire department in Australia to highlight the need and value of IoT and data interoperability. Dynamic Self Organizing Map based unsupervised algorithms which satisfy the requirements are described and further empirical results are presented to validate the required functionality of these algorithms.
机译：物联网（IoT）预计将在2020年连接204亿台设备，到2025年将激增至750亿个。在这种互联世界中，机器将与其他机器进行通信将为智能家居带来巨大的机遇和截然不同的生活方式，自动驾驶汽车和可穿戴设备。期望这种相互联系将使事件能够实时捕获为数据，并为人员和组织提供可行的见解，以最大程度地提高效率，积极主动和提高效率。互连的设备将需要互操作性，并且设备与应用程序之间的无缝，安全和受控的数据交换被称为数据互操作性。这种动态且易变的环境具有广泛的数据多样性，这将需要一种新型的智能算法，这些算法必须具有适应性和自学习能力，并且可以设想和分析多个抽象级别的事件以衡量关联和相互关系。这项研究提出了在这种物联网环境中智能算法的三个算法要求：无监督的自学习能力，向环境自我生成以及随着时间变化而逐步学习的能力。本文首先介绍了来自澳大利亚消防局的真实数据的实证结果，以强调物联网和数据互操作性的需求和价值。描述了满足要求的基于动态自组织图的无监督算法，并提供了进一步的经验结果，以验证这些算法的所需功能。

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  • 来源
    《Future generation computer systems》 |2018年第9期|421-432|共12页
  • 作者

    Rashmika Nawaratne; Damminda Alahakoon; Daswin De Silva; Prem Chhetri; Naveen Chilamkurti;

  • 作者单位

    Research Centre for Data Analytics and Cognition, La Trobe University;

    Research Centre for Data Analytics and Cognition, La Trobe University;

    Research Centre for Data Analytics and Cognition, La Trobe University;

    School of Business IT and Logistics, RMIT University;

    Computer Science and Computer Engineering, La Trobe University;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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