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RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes

机译:基于RSSI的智能家居ZigBee无线传感器网络的室内定位和识别

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Location-based services have increased in popularity in recent years and can be fruitfully exploited in the field of smart homes, opening the doors to a wide range of personalized services. In this context, radio technology can be widely employed since, other than connecting devices in the home system, it offers solutions for the user localization issue without the need of any extra device. Techniques based on received signal strength indicator (RSSI) are often used, relying on fingerprinting or proximity algorithms. In this paper, a novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis. Moreover, the actual user location is estimated from his/her interaction with the home system devices deployed in the house: if the home environment is inhabited by more than one person, it becomes of utmost importance the identification of who is actually interacting with a given device. A proximity method is exploited for this purpose. Tests have been carried out to characterize the approach, particularly, the effects of RSSI samples, number and position, of the anchor nodes have been analyzed. Finally, some considerations about power consumption of the mobile node have been presented.
机译:近年来,基于位置的服务日益普及,并且可以在智能家居领域得到有效利用,从而为各种个性化服务打开了大门。在这种情况下,无线电技术可以广泛使用,因为除了连接家庭系统中的设备以外,它还为用户本地化问题提供了解决方案,而无需任何额外的设备。经常使用基于接收信号强度指示器(RSSI)的技术,这些技术依赖于指纹识别或接近算法。本文提出了一种新的基于RSSI的房间级定位指纹识别方法:这是一种基于接收器工作特性分析的阈值算法。此外,实际用户位置是根据他/她与部署在房屋中的家庭系统设备的交互来估算的:如果家庭环境中居住着多个人,那么识别谁实际上正在与给定交互至关重要。设备。为此采用了一种接近方法。已经进行了测试以表征该方法,特别是分析了RSSI样本,锚节点的数量和位置的影响。最后,提出了有关移动节点功耗的一些考虑。

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