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ZiLoc: Energy efficient WiFi fingerprint-based localization with low-power radio

机译:ZiLoc:具有节能功能的基于WiFi指纹的低功耗无线电定位

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摘要

Indoor localization is essential to enable location-based services in wireless pervasive computing environment. In recent years, WiFi fingerprint-based localization has received considerable attention due to its deployment practicability. In order to achieve on-the-fly localization, WiFi receivers (e.g., mobile phones or laptops) being located need to scan WiFi signals continuously. Since they are normally battery driven, energy efficiency is a very important consideration in WiFi fingerprinting localization systems. Motivated by the fact that IEEE 802.11 (WiFi) and 802.15.4 (ZigBee) channels overlap in the 2.4GHz ISM band, in this work, we develop a WiFi fingerprint-based localization system using ZigBee radio, called ZiLoc. We first present a novel RSS-location fingerprint model to identify the features of surrounding APs. We then propose a simple yet effective method to compute the similarity of two RSS fingerprints. Experimental results demonstrate that ZiLoc can achieve an average of 85% room-level localization accuracy and reduce more than 60% energy consumption compared with the method using WiFi interfaces to collect RSS fingerprints.
机译:室内本地化对于在无线普及计算环境中启用基于位置的服务至关重要。近年来,基于WiFi指纹的本地化由于其实用性而备受关注。为了实现即时定位,所定位的WiFi接收器(例如手机或笔记本电脑)需要连续扫描WiFi信号。由于它们通常由电池驱动,因此能效是WiFi指纹定位系统中非常重要的考虑因素。出于IEEE 802.11(WiFi)和802.15.4(ZigBee)通道在2.4GHz ISM频段中重叠的事实的推动,在这项工作中,我们使用ZigBee无线电开发了基于WiFi指纹的定位系统,称为ZiLoc。我们首先提出一种新颖的RSS位置指纹模型,以识别周围AP的特征。然后,我们提出了一种简单而有效的方法来计算两个RSS指纹的相似度。实验结果表明,与使用WiFi接口收集RSS指纹的方法相比,ZiLoc可以实现平均85%的房间定位精度,并减少60%以上的能耗。

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