BlueDetect: An iBeacon-Enabled Scheme for Accurate and Energy-Efficient Indoor-Outdoor Detection and Seamless Location-Based Service

Zou, Han; Jiang, Hao; Luo, Yan; Zhu, Jianjie; Lu, Xiaoxuan; Xie, Lihua

doi:10.3390/s16020268

Cited by 95 publications

(63 citation statements)

References 32 publications

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“…Several recent works have attempted to deal with indoor-outdoor detection. BlueDetect [3] is based on the emerging iBeacon technology and excels in its low power consumption but relies on the installation of specific hardware on-site. Wang et al [4] uses the Global System for Mobile (GSM) communication cellular base station's signal strength to attack the problem and achieves high accuracy, however the method was only tested using a single device.…”

Section: Indoor-outdoor Detectionmentioning

confidence: 99%

Clustering Wi-Fi fingerprints for indoor–outdoor detection

2018

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This paper presents a method for continuous indoor-outdoor environment detection on mobile devices based solely on Wi-Fi fingerprints. Detection of indoor-outdoor switching is an important part of identifying a user's context, and it provides important information for upper layer context aware mobile applications such as recommender systems, navigation tools, etc. Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. In contrast to existing research, we believe that these fingerprints should be leveraged, and they serve as the basis of the proposed method. Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. The contribution of each of the features to the method is assessed. Our method assumes no prior knowledge of the environment, and a training set consisting of the data collected for just a few hours on a single device is sufficient in order to provide indoor-outdoor classification, even in an unknown location or when using new devices. We evaluate our method in an experiment involving 12 participants during their daily routine, with a total of 828 hours' worth of data collected by the participants. We report a predictive performance of the AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble learning method. We show that our method can be used for other context detection tasks such as learning and recognizing a given building or room.

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Section: Indoor-outdoor Detectionmentioning

confidence: 99%

Clustering Wi-Fi fingerprints for indoor–outdoor detection

2018

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“…They have applied cotraining methods and managed to achieve 92% of accuracy compared to GPS and IODetector. Other related work can be found in [17], [18].…”

Section: Sensor Fusion Based Environment Classification a Indoormentioning

confidence: 99%

Extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data

Lau

Marakkalage

Kadaba

et al. 2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Abstract-The advancement of smartphones with various type of sensors enabled us to harness diverse information with crowd sensing mobile application. However, traditional approaches have suffered drawbacks such as high battery consumption as a trade off to obtain high accuracy data using high sampling rate. To mitigate the battery consumption, we proposed low sampling point of interest (POI) extraction framework, which is built upon validation based stay points detection (VSPD) and sensor fusion based environment classification (SFEC). We studied various of clustering algorithm and showed that density based spatial clustering of application with noise (DBSCAN) algorithms produce most accurate result among existing methods. The SFEC model is utilized for classifying the indoor or outdoor environment of the POI clustered earlier by VSPD. Real world data are collected, benchmarked using existing clustering method to denote effectiveness of low sampling rate model in high noise spatial temporal data.

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“…The explosion of outdoor location-based services (OLBSs) in the Internet of Things (IoT) has stimulated extensive research efforts in recent years [1]. Traditional OLBSs rely on satellite navigation positioning systems, such as the Global Positioning System (GPS), and they bring great benefits and convenience to many people.…”

Section: Introductionmentioning

confidence: 99%

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Lei

Zhang

2020

Sensors

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In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.technologies require additional devices, making these systems impractical. Additionally, in signal NLOS propagation, the performance of the positioning systems will be greatly reduced [5].In contrast, wireless fingerprint positioning technology has received much attention owing to its simplicity and practicality with existing infrastructure and hardware. Many complex LTE signal cues are hidden in the surrounding environment, and the goal of wireless fingerprint-based localization is to discover these cues and use them effectively to determine UE positions [6]. Compared to satellite navigation positioning systems and other wireless positioning technologies, fingerprint-based positioning techniques have many merits. First, low-energy consumption sensors in UE require much less energy. Second, most of the fingerprint-based positioning technologies require no additional devices or infrastructure. Finally, fingerprint-based positioning can be achieved by using ubiquitous smartphones and signals from LTE base stations (BSs) [6,7]. Fingerprint positioning technology works in two phases: an offline training phase and an online matching phase [8]. During the training phase, signal measurements can be used, which are usually received signal strength indicator (RSSI), reference signal receivin...

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BlueDetect: An iBeacon-Enabled Scheme for Accurate and Energy-Efficient Indoor-Outdoor Detection and Seamless Location-Based Service

Cited by 95 publications

References 32 publications

Clustering Wi-Fi fingerprints for indoor–outdoor detection

Clustering Wi-Fi fingerprints for indoor–outdoor detection

Extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

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