A Framework for Mining Actionable Navigation Patterns from In-Store RFID Datasets via Indoor Mapping

Shen, Bin; Zheng, Qiuhua; Li, Xingsen; Xu, Libo

doi:10.3390/s150305344

Cited by 18 publications

(14 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Received signal strength (RSS) is the relative quality of a received signal for a device, which is related to the distance between the tags and antenna for RFID, and has been previously utilized for indoor localization systems (ILSs) [13][14][15][16], indoor mapping and navigation in retail stores [17], indoor tracking system for elderly [18], and gesture recognition [19]. Previous studies confirm RSS approaches to be suitable to detect false positives [20][21][22], where static and moved tags were classified as false positive and true positive readings, respectively.…”

Section: Introductionmentioning

confidence: 99%

False Positive RFID Detection Using Classification Models

et al. 2019

View full text Add to dashboard Cite

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

show abstract

Section: Introductionmentioning

confidence: 99%

False Positive RFID Detection Using Classification Models

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Radio frequency identification (RFID) [ 12 ] is the key technology necessary to realize the Internet of Things (IoT) and cyber-physical systems (CPS), widely used in health monitors [ 13 , 14 ], smart homes [ 15 ], smart cities [ 16 ], vehicle location [ 17 ], construction [ 18 ], supply chain management [ 19 ] and object tracking [ 20 ], etc. RFID has the advantages of low cost, long life, low power consumption and easy deployment, which attracts many researchers to use it in indoor environments [ 21 , 22 , 23 ]. Compared with ultrasonic, WiFi and Bluetooth, RFID used in indoor positioning is more cost and energy efficient.…”

Section: Introductionmentioning

confidence: 99%

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor

Ding

et al. 2017

Sensors

150

View full text Add to dashboard Cite

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

show abstract

“…By using the protocol for communication between user tags and fixed readers near the counter [8]), RFID readers can detect the existence of nearby tags. The queuing parameters can be tracked based on the number of detected tags and signal strength [9].…”

Section: Introductionmentioning

confidence: 99%

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

Shu

Pei

et al. 2016

Sensors

View full text Add to dashboard Cite

Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

show abstract

A Framework for Mining Actionable Navigation Patterns from In-Store RFID Datasets via Indoor Mapping

Cited by 18 publications

References 38 publications

False Positive RFID Detection Using Classification Models

False Positive RFID Detection Using Classification Models

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

Contact Info

Product

Resources

About