RFID Indoor Localization Using Master-Slave Reference Tags Scheme for Manufacturing Environment

2014 ◽  
Author(s):  
Shilong Zhang ◽  
Quan Liu ◽  
Wenjun Xu ◽  
Zaiqun Liu
Keyword(s):  
Real Time ◽  
Manufacturing Process ◽  
Indoor Localization ◽  
Physical Object ◽  
Location Information ◽  
Localization Algorithm ◽  
System Load ◽  
Indoor Location ◽  
Time Performance ◽  
Reference Tags

In manufacturing process, the indoor location information of physical object is an essential part in storage and transport link. The efficient perception of indoor location is able to significantly reduce the system load and also improves its real-time performance. In this paper, a novel RFID indoor localization algorithm using Master-Slave reference tags scheme (MSRT) is presented. The algorithm divides the sensing area into several subspaces with master reference tags to realize rough location. In each subspaces, slave reference tags are used to perform partial location. A set of experiments have been conducted and the results demonstrate that the proposed method can reduce system redundancy and server load without decrement of accuracy.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

scholarly journals Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xinlong Jiang ◽  
Yiqiang Chen ◽  
Junfa Liu ◽  
Dingjun Liu ◽  
Yang Gu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Indoor Localization ◽  
High Accuracy ◽  
Motion Sensors ◽  
Fusion Model ◽  
Indoor Location ◽  
Smooth Tracking ◽  
Error Accumulation ◽  
Learning Machine

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.

scholarly journals Access control system using beacon-based indoor location data pattern analysis

2018 ◽  
Vol 7 (2.12) ◽  
pp. 325
Author(s):  
Jae Gwang Lee ◽  
Jae Pil Lee ◽  
Eun Su Mo ◽  
Jun Hyeon Lee ◽  
Ki Su Yoon ◽  
...  
Keyword(s):  
Control System ◽  
Access Control ◽  
Real Time ◽  
Pattern Analysis ◽  
Behavior Pattern ◽  
Abnormal Behavior ◽  
Location Information ◽  
Indoor Location ◽  
Location Data ◽  
Access Control System

Background/Objectives: With the development of IT, accidents of industrial secret leakage have occurred more than before. Such acci-dents are mostly caused by insiders. Methods/Statistical analysis: An existing access control system uses RFID and NFC tag. The system saves only the final location in-formation in DB. For the reason, it is hard to track a user’s location data in real time. However, a beacon-based access control system saves a user’s location information in DB in real time. By analyzing the location information of DB, it is possible to track a user. Findings: Beacons are used for determining a user’s location. The determined location information is converted into location data which is saved into DB. The location data is converted into coordinates. The converted coordinate data is analyzed for understanding a user’s behavior pattern. In the pattern analysis, if a user takes an abnormal behavior, policy-based response is performed. The user behavior pattern analysis system proposed in this study is able to respond to an accident in real time. Therefore, it is expected to contribute to reducing the number of industrial secret leakage accidents caused by insiders. Improvements/Applications: This study designs a model that analyzes behavior pattern by using the indoor location data of a user based on beacon. 

scholarly journals Fine-grained wireless propagation ambience sensing

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880469
Author(s):  
Nan Jing ◽  
Yu Sun ◽  
Lin Wang ◽  
Jinxin Shan
Keyword(s):  
Real Time ◽  
Fresnel Zone ◽  
Signal Propagation ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Mobile Environment ◽  
The Real ◽  
Time Performance ◽  
Wireless Signal ◽  
Non Line Of Sight

The ubiquitous wireless network infrastructure and the need of people’s indoor sensing inspire the work leveraging wireless signal into broad spectrum for indoor applications, including indoor localization, human–computer interaction, and activity recognition. To provide an accurate model selection or feature template, these applications take the system reliability of the signal in line-of-sight and non-line-of-sight propagation into account. Unfortunately, these two types of signal propagation are analyzed in static or mobile scenario separately. Our question is how to use the wireless signal to estimate the signal propagation ambience to facilitate the adaptive complex environment? In this paper, we exploit the Fresnel zone theory and channel state information (CSI) to model the static and mobile ambience detectors. Considering the spatiotemporal correlation of indoor activities, the propagation ambience can be divided into three categories: line-of-sight (LOS), non-line-of-sight (NLOS), and semi-line-of-sight (SLOS), which is used to represent the intermediate state between the LOS and NLOS propagation ambience during user movement. Leveraging the hidden Markov model to estimate the dynamic propagation ambience in the mobile environment, a novel propagation ambience identification method, named Ambience Sensor (Asor), is proposed to improve the real-time performance for the upper applications. Furthermore, Asor is integrated into a localization algorithm, Asor-based localization system (Aloc), to confirm the effectiveness. We prototype Asor and Aloc based on commodity WiFi infrastructure without any hardware modification. In addition, the real-time performance of Asor is evaluated by conducting tracking experiments. The experimental results show that the median detection rate of propagation ambience is superior to the existing methods in absence of any a priori hypothesis of static or mobile scenarios.

scholarly journals Indoor Localization Using Deep-Learning and Smartphone

2019 ◽  
pp. 40-49
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Indoor Localization ◽  
Learning Phase ◽  
Second Phase ◽  
Indoor Location ◽  
Online Processing ◽  
Offline Learning ◽  
Proposed Model ◽  
Matching Techniques

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. In the building where the GPS is not available, this project can be utilized. Indoor localization based on image matching techniques related to deep learning was achieved in a hard environment. So, if it wanted to raise the precision of indoor classification, the number of image dataset of the indoor environment should be as large as possible to satisfy and cover the underlying area. In this work, a smartphone camera is used to build the image-based dataset of the investigated building. In addition, captured images in real time are taken to be processed with the proposed model as a test set. The proposed indoor localization includes two phases the first one is the offline learning phase and the second phase is the online processing phase. In the offline learning phase, here we propose a convolutional neural network (CNN) model that sequences the features of image data from some classis's dataset composed with a smartphone camera. In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appoint the expected indoor location. The proposed system has been tested over various experiments and the obtained experimental results show that the accuracy of the prediction is almost 98.0%.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance
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