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Event-Aware Framework for Dynamic Services Discovery and Selection in the Context of Ambient Intelligence and Internet of Things

机译:在环境智能和物联网环境下用于动态服务发现和选择的事件感知框架

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摘要

The Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users’ requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment.
机译:物联网是环境智能的自然连续性,主要通过集成大量互连的智能对象(传感器,执行器,智能手机,设备等)以及抽象为软件服务的异构功能来构建智能和周围环境。这些服务可以随时随地进行组合,并随时随地提供,以帮助用户进行日常活动。以用户为中心的服务组合中的一个关键问题是智能,有效地发现和选择最能满足用户要求并紧密满足指定服务质量级别的最相关服务。无缝监视提供的服务并提高其质量仍然是一个具有挑战性的问题,这主要是由于周围环境的动态性和不确定性所致。在本文中,我们提出了一种新的面向服务,以用户为中心和事件感知的框架,该框架能够执行服务监视以自动处理在环境中可能发生的事件。此监视基于动态的服务发现和选择过程,以增强对不可预测的更改的自适应,并确保以最佳质量提供服务连续性。通过专门针对环境辅助生活环境中的日常活动进行识别的方案,已实施并验证了总体建议框架。此外,从大量测试中获得的性能清楚地表明了在大规模环境下该方法的效率和可行性。

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