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Context-awareness for mobility management: A systems survey for healthcare monitoring

机译:用于移动管理的上下文感知:用于医疗保健监控的系统调查

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The advent of supplying mobile devices with more processing capabilities, employing reduced power consumption techniques and incorporating the latest data transmission technologies sets ground to a vast variety of applications. Information technology has been playing an increasingly stronger part on the healthcare area throughout the years, aiding it to be more accurate, faster to respond and less sensible to human errors. Wireless access technology has a vast application on Healthcare, namely for living assistance at patient's home or on the move, performing such tasks as monitoring patient's health. This concept of health monitoring can be further extended to sport performance monitoring or risk job health monitoring (for example: mine workers, track runners) to whom stationary monitoring is not possible. To support this resiliency with extended battery life, the monitoring terminal requires an efficient management of radio access technologies that enable it to communicate. This paper provides a literature review on context aware systems for mobility management, which are applied to aid the handover process. Based on learning data, users' connectivity can be optimized. Deploying entities that have functional intelligence and consistent background knowledge at their disposal, it will enable taking clever decisions therefore improving its performance. Ideally, these entities will be able to learn from experience, with special regard to user preferences, user behavior and recurring situations in the network infrastructure and surrounding scenario.
机译:为移动设备提供更多处理能力,采用降低功耗的技术并结合最新的数据传输技术的问世为各种应用奠定了基础。多年来,信息技术在医疗保健领域一直发挥着越来越重要的作用,这有助于信息技术更加准确,响应速度更快,对人为错误的敏感性降低。无线访问技术在医疗保健领域具有广泛的应用,即用于患者家中或旅途中的生活辅助,执行诸如监视患者健康的任务。健康监控的概念可以进一步扩展到无法进行固定监控的运动表现监控或冒险工作健康监控(例如:矿山工人,田径运动员)。为了通过延长电池寿命来支持这种弹性,监视终端需要对无线电访问技术进行有效管理,以使其能够进行通信。本文提供了有关用于移动性管理的上下文感知系统的文献综述,该系统可用于协助切换过程。基于学习数据,可以优化用户的连接性。部署具有功能智能和一致背景知识的实体,这将使您能够做出明智的决策,从而提高其绩效。理想情况下,这些实体将能够从经验中学习,特别是在网络基础结构和周围环境中的用户偏好,用户行为和重复出现的情况。

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