scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

scholarly journals RSS-based Indoor Positioning Using Convolutional Neural Network

2020 ◽  
Vol 16 (12) ◽  
pp. 82
Author(s):  
Safae El Abkari ◽  
Abdelilah Jilbab ◽  
Jamal El Mhamdi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Wireless Sensor ◽  
Positioning System ◽  
Improve Accuracy ◽  
Random Fluctuations ◽  
Indoor Positioning System

<p class="0abstract"><span lang="EN-US">Indoor Positioning has come under the spotlight in the last decade due to the increasing of location-based services demands. RSS Wi-Fi based positioning using the fingerprinting technique is widely used due to its low hardware requirements and simplicity. However, multi-path and fading cause random fluctuations of collected RSS values which affects the positioning accuracy. For this purpose, we propose an indoor positioning system based on RSS and convolutional neural network. This approach aims to improve accuracy by reducing the noise and the randomness of collected RSS values from a wireless sensor network. We implemented and evaluated our system using a single floor and multi-grid dataset. Our proposed approach provides a room and grid prediction accuracies of 100% and a mean error of location estimation of 0.98 m.</span></p>

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

scholarly journals A REVIEW ON INDOOR LOCALIZATION WITH INTERNET OF THINGS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 121-128
Author(s):  
C. Basri ◽  
A. Elkhadimi
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Location Based Services ◽  
Positioning System ◽  
Resources Management ◽  
Indoor Positioning System

Abstract. The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.

WiFi and Bluetooth Low Energy Beacons for Indoor Positioning System

2019 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Nurul Fatehah Zulkpli ◽  
Nor Azlina Ab. Aziz ◽  
Noor Ziela Abd Rahman ◽  
Rosli Besar
Keyword(s):  
Indoor Positioning ◽  
Low Energy ◽  
Location Based Services ◽  
Positioning System ◽  
Indoor Environments ◽  
Wireless Technologies ◽  
Bluetooth Low Energy ◽  
Advantages And Disadvantages ◽  
Performance Accuracy ◽  
Indoor Positioning System

Indoor Positioning System (IPS) is used to locate a person, an object or a location inside a building. IPS is important in providing location-based services, which has recently gain much popularity. The services ease visitors’ navigation at unfamiliar premises. Location-based services depend on the capability of IPS to accurately determine the location of the user, which is a challenging issue in indoor environments. Several wireless technologies are available. In this paper, two of the most widely used IPS technologies are reviewed which are, WiFi and Bluetooth low energy (BLE). Their advantages and disadvantages are reviewed and reported here. Comparison of the systems based on their performance, accuracy and limitations are presented as well.

A Context-Aware Mobility Indoor Positioning System

2015 ◽  
Vol 6 (1) ◽  
pp. 25-47
Author(s):  
Eugene Ferry ◽  
John O'Raw ◽  
Kevin Curran
Keyword(s):  
Mobile Device ◽  
Local Area Network ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Area Network ◽  
Positioning System ◽  
Context Aware ◽  
Production Environment ◽  
System Solution ◽  
Indoor Positioning System

The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11. The authors demonstrate here a real world application of determining one's location with the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. They detail their implementation of an Android application which communicates with the Cisco Context-Aware Mobility system to visually display the location of the mobile device. The application was tested in a production environment and limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified. The authors found that to obtain optimal accuracy, a device must be detected by four or more Access points so a recommended distribution for an indoor positioning system built on the Cisco context-aware mobility framework is for an Access Point to be placed every 12 – 20 linear meters.

scholarly journals Platform and Algorithm Development for a RFID-Based Indoor Positioning System

2014 ◽  
Vol 02 (03) ◽  
pp. 279-291 ◽  
Author(s):  
Han Zou ◽  
Lihua Xie ◽  
Qing-Shan Jia ◽  
Hengtao Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Support Vector ◽  
Rfid Tags ◽  
Positioning System ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Reference Tags ◽  
Indoor Positioning System

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

Amp-Phi: A CSI-Based Indoor Positioning System

2018 ◽  
Vol 32 (09) ◽  
pp. 1858005 ◽  
Author(s):  
Tao-Yun Zhou ◽  
Bao-Wang Lian ◽  
Yi Zhang ◽  
Sen Liu
Keyword(s):  
Nearest Neighbor ◽  
Phase Error ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Positioning System ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Error Mitigation ◽  
Indoor Positioning System

With rapid growth in the demand of location-based services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interest due to their convenience. Until now, most such methods were based on received signal strength indicator (RSSI), which is vulnerable to non-line-of-sight (NLOS). In order to realize high-precision indoor positioning, we propose a channel state information (CSI)-based Amp-Phi indoor-positioning system which exploits the amplitude and phase information of CSI at the same time to establish a fingerprinting database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build an NLOS mitigation model and a phase error mitigation model, respectively, to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and machine learning (ML) systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, by doctors or patients in the hospital, for people localization in large supermarkets or museums and so on.

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