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Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes

机译:基于传感器网络的用户友好型用户位置发现功能,可用于未来的智能家居

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User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users’ privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation.
机译:用户位置是未来智能家居的关键上下文信息,未来的智能家居将提出许多基于位置的服务。此位置必然意味着用户位置发现(ULD)将在未来的智能家居中扮演重要角色。当前,由于担心隐私以及在智能家居中携带移动设备或标签设备的需求,传统的ULD系统使用户感到不舒服。未来的智能家居将需要ULD系统来考虑这些挑战。本文讨论了在未来的智能家居中用于上下文感知服务的ULD系统的设计,强调了以下挑战:(i)用户的隐私; (ii)无装置/无标签; (iii)容错性和准确性。另一方面,物联网,嵌入式系统,智能设备和机器对机器通信等新兴新技术正渗透到我们的日常生活中,越来越多的传感器可用于家庭。考虑到这一机会,我们提出了一种ULD系统,该系统可在满足上述挑战的同时,充分利用家用传感器的普及率。所提出的基于传感器网络且用户友好的ULD系统依赖于不同类型的廉价传感器,以及具有基于模糊决策者的上下文代理。上下文代理从不同类型的传感器接收上下文信息,并使用模糊集理论评估该数据。我们通过说明一个用例,同时利用分析模型和仿真,来演示所提出系统的性能。

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