3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning

As the core supporting technology of the Internet of Things, Radio Frequency Identification (RFID) technology is rapidly popularized in the fields of intelligent transportation, logistics management, industrial automation, and the like, and has great development potential due to its fast and efficient data collection ability. RFID technology is widely used in the field of indoor localization, in which three-dimensional location can obtain more real and specific target location information. Aiming at the existing three-dimensional location scheme based on RFID, this paper proposes a new three-dimensional localization method based on deep learning: combining RFID absolute location with relative location, analyzing the variation characteristics of the received signal strength (RSSI) and Phase, further mining data characteristics by deep learning, and applying the method to the smart library scene. The experimental results show that the method has a higher location accuracy and better system stability.

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Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

Sensors ◽

10.3390/s20236756 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6756

Author(s):

DongHyun Ko ◽

Seok-Hwan Choi ◽

Sungyong Ahn ◽

Yoon-Ho Choi

Keyword(s):

Deep Learning ◽

Random Forest ◽

Prediction Accuracy ◽

Indoor Localization ◽

Artificial Noise ◽

Good Prediction ◽

Localization Method ◽

Good Prediction Accuracy ◽

Attack Scenario ◽

Natural Noise

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

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Unsupervised Topographic Learning for Spatiotemporal Data Mining

Advances in Artificial Intelligence ◽

10.1155/2010/832542 ◽

2010 ◽

Vol 2010 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Guénaël Cabanes ◽

Younès Bennani

Keyword(s):

Data Mining ◽

Radio Frequency Identification ◽

Spatiotemporal Data ◽

Rfid Technology ◽

Spatiotemporal Information ◽

Behavioral Studies ◽

Spatiotemporal Data Mining ◽

Efficient Data ◽

Frequency Identification ◽

Mining Tool

In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.

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A Channel Adaptive WiFi Indoor Localization Method based on Deep Learning

2021 IEEE Wireless Communications and Networking Conference (WCNC) ◽

10.1109/wcnc49053.2021.9417310 ◽

2021 ◽

Author(s):

Lifei Hao ◽

Baoqi Huang ◽

Hao Hong ◽

Bing Jia ◽

Wuyungerile Li

Keyword(s):

Deep Learning ◽

Indoor Localization ◽

Localization Method

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Mobile Drone Localization in Indoor Environment Based on Passive RFID

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v14i05.13309 ◽

2020 ◽

Vol 14 (05) ◽

pp. 4

Author(s):

Mohamed Hadi Habaebi ◽

Rashid Khamis Omar ◽

Md Rafiqul Islam

Keyword(s):

Radio Frequency Identification ◽

Information Exchange ◽

Indoor Environment ◽

Indoor Localization ◽

Localization Accuracy ◽

Localization Method ◽

Mobile Objects ◽

Frequency Identification ◽

Passive Rfid ◽

Rfid Readers

<p class="AEEEAbstract">Radio Frequency Identification (RFID) is an information exchange technology based on RF communication. It provides solution to track and localize mobile objects in the indoor environment. Localization of mobile objects in an indoor environment garnered a significant attention due to the variety of applications needing higher degree of localization accuracy. RSS-based localization techniques are the major tools for tracking applications due to their simplicity. In this paper, a trilateration method for indoor localization is proposed. This method provides a solution for the drone tracking problem by collecting the RSS values between RFID tagged drone and reader, and estimate its location. The localization method is implemented in MATLAB by multiple readers; 4 RFID readers and 8 RFID readers. The performance of the localization method is also compared with other RFID localization previously reported in the literature. The simulation results in the case of 8 RFID readers demonstrate more accurate results than 4 RFID readers by minimizing the localization error from 0.84606 to 0.40079m. The results also indicate an improved localization performance of tracking a tagged drone in indoor environment by 42.8% when 8RFID readers are placed in the localization area.</p>

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A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning

Applied Sciences ◽

10.3390/app10113803 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3803

Author(s):

Jiuchao Qian ◽

Yuhao Cheng ◽

Rendong Ying ◽

Peilin Liu

Keyword(s):

Indoor Localization ◽

Dimensional Space ◽

Three Dimensional ◽

Dead Reckoning ◽

Indoor Navigation ◽

Two Dimensional ◽

Localization Method ◽

Practical Applications ◽

Cumulative Error ◽

Starting Point

Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it limited in practical applications. In this paper, a novel localization method V-PDR is presented, which combines VPR (Visual Place Recognition) and PDR in a loosely coupled way. When there is error between the localization result of PDR and VPR, the algorithm will correct the localization of PDR, which significantly reduces the cumulative error. In addition, VPR recognizes scenes on different floors to correct floor localization due to vertical movement, which extends application scene of PDR from two-dimensional planes to three-dimensional spaces. Extensive experiments were conducted in our laboratory building to verify the performance of the proposed method. The results demonstrate that the proposed method outperforms general PDR method in accuracy and can work in three-dimensional space.

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An Indoor Localization Method of Image Matching Based on Deep Learning

Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) ◽

10.2991/mecae-18.2018.21 ◽

2018 ◽

Cited By ~ 1

Author(s):

Guihua Yang ◽

Yu Liang

Keyword(s):

Deep Learning ◽

Image Matching ◽

Indoor Localization ◽

Localization Method

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A New RFID-Identification Strategy Applied to the Marble Extraction Industry

Electronics ◽

10.3390/electronics10040491 ◽

2021 ◽

Vol 10 (4) ◽

pp. 491

Author(s):

Leonardo Andrade ◽

João Figueiredo ◽

Mouhaydine Tlemçani

Keyword(s):

Radio Frequency Identification ◽

Dynamic Capabilities ◽

Three Dimensional ◽

Rfid Technology ◽

Production Chain ◽

Frequency Identification ◽

Non Destructive ◽

Transformation Processes ◽

Laboratory Prototype ◽

Identification Strategy

This paper aims to improve the marble industry production chain by proposing new technological approaches using the Radio Frequency Identification (RFID) systems. The dynamic capabilities of the RFID read-write tags allow the storage of physical characteristics of stone blocks, according to electrical, ultrasound and three-dimensional image characterization tests. These characterization non-destructive tests allow the evaluation of important parameters of the original stone blocks, by analyzing the internal structure of the rocks. Then, these parameters can be stored in databases through RFID-tags, in order to optimize their subsequent cutting and transformation processes. RFID identification technology when integrated into an ethernet communication network enables automatic communication with cutting and processing equipment, building an intelligent industrial platform, integrating PCs (Personal Computers) and PLCs (Programmable Logic Controllers) within an Industry 4.0 environment. Another huge advantage of RFID technology is that it allows full product traceability, namely by enabling the end consumer to reverse the production path. A laboratory prototype was implemented and a detailed analysis and discussion of the obtained functionalities is shown at the end of this paper.

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