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SmartFix: Indoor Locating Optimization Algorithm for Energy-Constrained Wearable Devices

机译:SMARTFIX:用于能量受限可穿戴设备的室内定位优化算法

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摘要

Indoor localization technology based on Wi-Fi has long been a hot research topic in the past decade. Despite numerous solutions, new challenges have arisen along with the trend of smart home and wearable computing. For example, power efficiency needs to be significantly improved for resource-constrained wearable devices, such as smart watch and wristband. For a Wi-Fi-based locating system, most of the energy consumption can be attributed to real-time radio scan; however, simply reducing radio data collection will cause a serious loss of locating accuracy because of unstable Wi-Fi signals. In this paper, we present SmartFix, an optimization algorithm for indoor locating based on Wi-Fi RSS. SmartFix utilizes user motion features, extracts characteristic value from history trajectory, and corrects deviation caused by unstable Wi-Fi signals. We implemented a prototype of SmartFix both on Moto 360 2nd-generation Smartwatch and on HTC One Smartphone. We conducted experiments both in a large open area and in an office hall. Experiment results demonstrate that average locating error is less than 2 meters for more than 80% cases, and energy consumption is only 30% of Wi-Fi fingerprinting method under the same experiment circumstances.
机译:基于Wi-Fi的室内定位技术长期以来一直是过去十年的热门研究课题。尽管解决了众多的解决方案,但出现了新的挑战以及智能家居和可穿戴计算的趋势。例如,对于资源受限的可佩戴设备,例如智能手表和腕带,需要显着改善功率效率。对于基于Wi-Fi的定位系统,大部分能量消耗可归因于实时无线电扫描;然而,由于不稳定的Wi-Fi信号,简单地减少无线电数据收集将导致定位精度的严重损失。本文介绍了SmartFix,基于Wi-Fi RSS的室内定位优化算法。 SmartFix利用用户运动功能,从历史轨迹中提取特征值,并纠正由不稳定的Wi-Fi信号引起的偏差。我们在Moto 360第2代SmartWatch和HTC One智能手机上实现了SmartFix的原型。我们在大型开放区域和办公室大厅进行了实验。实验结果表明,超过80%的情况下平均定位误差小于2米,在相同的实验情况下,能耗仅为Wi-Fi指纹方法的30%。

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