TinyLoc:一种面向能耗受限的可穿戴设备的室内定位算法
发布时间：2019-07-03 15:39
【摘要】：近年来,基于Wi-Fi接收信号强度的室内定位技术一直是研究领域的热点问题.随着智能家居和可穿戴计算的高速发展,大量新型智能设备的出现进一步推动了室内定位技术的发展,同时也带来了新的挑战.可穿戴设备与传统智能设备相比,有着与用户更加紧密的位置绑定关系,是一类更加适合的室内定位平台.但另一方面,类似智能手表、眼镜、手环以及戒指等可穿戴设备,由于其自身资源受限的特性,迫切需要一种低功耗的新型室内定位算法.通过本文在Moto 360二代智能手表上进行的实验可以发现,基于Wi-Fi RSS的室内定位服务会使智能手表的使用时间缩短82%以上,其中99%的定位能耗,是为了保证定位精度而大量进行射频信号采集所造成的.简单的减少信号采集量将带来显著的定位精度下降,如何在保障定位精度的前提下,尽可能地减少信号采集量是低功耗定位技术面临的核心挑战.该文提出了一种面向能耗受限的可穿戴设备的室内定位技术TinyLoc.TinyLoc在实时定位阶段仅需要一次信号采集,同时运用用户运动特性弥补信号采集量减少而带来的精度缺失.实验结果表明,在90%的情况下,TinyLoc对于完整路径上的点平均误差可以达到3m以内,另一方面,在相同实验环境下,TinyLoc能耗为传统Wi-Fi定位算法的1/6,是MoLoc的64%.相比传统的基于Wi-Fi信号的定位算法,TinyLoc可以延长Moto 360二代智能手表约3倍的定位工作时间.
[Abstract]:In recent years, indoor positioning technology based on Wi-Fi received signal strength has been a hot issue in the field of research. With the rapid development of smart home and wearable computing, the emergence of a large number of new intelligent devices has further promoted the development of indoor positioning technology, but also brought new challenges. Compared with traditional intelligent devices, wearable devices have closer location binding relationship with users, and it is a more suitable indoor positioning platform. On the other hand, wearable devices such as smartwatches, glasses, bracelets and rings are in urgent need of a new low-power indoor positioning algorithm because of their limited resources. Through the experiment on the second generation smartwatch of Moto 360, it can be found that the indoor positioning service based on Wi-Fi RSS can shorten the service time of smartwatch by more than 82%, of which 99% of the positioning energy consumption is caused by a large number of RF signal acquisition in order to ensure the positioning accuracy. A simple reduction of signal acquisition will lead to a significant decline in positioning accuracy. How to reduce signal acquisition as much as possible under the premise of ensuring positioning accuracy is the core challenge of low-power positioning technology. In this paper, an indoor positioning technology for wearable devices with limited energy consumption is proposed. TinyLoc.TinyLoc needs only one signal acquisition in the real-time positioning phase, and makes use of user motion characteristics to make up for the lack of accuracy caused by the reduction of signal acquisition. The experimental results show that the average error of TinyLoc for the point on the complete path can reach less than 3m in 90% case. On the other hand, in the same experimental environment, the energy consumption of TinyLoc is 1 鈮，
本文编号：2509495
[Abstract]:In recent years, indoor positioning technology based on Wi-Fi received signal strength has been a hot issue in the field of research. With the rapid development of smart home and wearable computing, the emergence of a large number of new intelligent devices has further promoted the development of indoor positioning technology, but also brought new challenges. Compared with traditional intelligent devices, wearable devices have closer location binding relationship with users, and it is a more suitable indoor positioning platform. On the other hand, wearable devices such as smartwatches, glasses, bracelets and rings are in urgent need of a new low-power indoor positioning algorithm because of their limited resources. Through the experiment on the second generation smartwatch of Moto 360, it can be found that the indoor positioning service based on Wi-Fi RSS can shorten the service time of smartwatch by more than 82%, of which 99% of the positioning energy consumption is caused by a large number of RF signal acquisition in order to ensure the positioning accuracy. A simple reduction of signal acquisition will lead to a significant decline in positioning accuracy. How to reduce signal acquisition as much as possible under the premise of ensuring positioning accuracy is the core challenge of low-power positioning technology. In this paper, an indoor positioning technology for wearable devices with limited energy consumption is proposed. TinyLoc.TinyLoc needs only one signal acquisition in the real-time positioning phase, and makes use of user motion characteristics to make up for the lack of accuracy caused by the reduction of signal acquisition. The experimental results show that the average error of TinyLoc for the point on the complete path can reach less than 3m in 90% case. On the other hand, in the same experimental environment, the energy consumption of TinyLoc is 1 鈮，
本文编号：2509495
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