scholarly journals Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3933
Author(s):  
Mohammed El-Absi ◽  
Feng Zheng ◽  
Ashraf Abuelhaija ◽  
Ali Al-haj Abbas ◽  
Klaus Solbach ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Channel Modeling ◽  
Low Complexity ◽  
Small Scale ◽  
Indoor Environments ◽  
Wave Band ◽  
Localization Algorithm ◽  
Localization Accuracy

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

scholarly journals RILS: RFID indoor localization system using mobile readers

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877128 ◽  
Author(s):  
Jinkai Liu ◽  
Yanqing Qiu ◽  
Kezhao Yin ◽  
Wentong Dong ◽  
Jiaqing Luo
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Scheduling Algorithm ◽  
Localization Accuracy ◽  
Localization System ◽  
Continuous Scanning ◽  
Reference Tags ◽  
Frequency Identification

The radio frequency identification technology was given greater interest as it is widely used for identification and localization in the cognitive radio sensor networks. While radio frequency identification–based indoor localization is attractive, the need for a large-scale and high-density deployment of readers and reference tags is costly. Using mobile readers mounted on guide rails, we design and implement an RFID indoor localization system, which requires neither reference tags nor received signal strength indicator functions, for stock-taking and searching in warehouse operations. In particular, we install two guide rails, which can allow a reader to move horizontally or vertically, on the ceiling of a warehouse or workshop. We then propose a continuous scanning algorithm to improve the accuracy for locating a single tagged object and a category-based scheduling algorithm to shorten the time for locating multiple tagged objects. Our primary experimental results show that RFID indoor localization system can achieve high time efficiency and localization accuracy in the indoor localization.

scholarly journals CP-NR Distributed Range Free Localization Algorithm in WSN

2018 ◽  
Vol 7 (3) ◽  
pp. 212
Author(s):  
Deepak Prashar ◽  
Kiran Jyoti ◽  
Dilip Kumar
Keyword(s):  
Large Scale ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Sensor Nodes ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Network Coverage ◽  
Localization Accuracy ◽  
Two Factors ◽  
Range Free

<p>Advancements in wireless communication technology have empowered the researchers to develop large scale wireless networks with huge number of sensor nodes. In these networks localization is very active field of research. Localization is a way to determine the physical position of sensor nodes which is useful in many aspects such as to find the origin of events, routing and network coverage.  Locating nodes with GPS systems is expensive, power consuming and not applicable to indoor environments. Localization in three dimensional space and accuracy of the estimated location are two factors of major concern. In this paper, a new three dimensional Distributed range-free algorithm which is known as CP-NR is proposed. This algorithm has high localization accuracy and resolved the problem of existing NR algorithm. CP-NR (Coplanar and Projected Node Reproduction) algorithm makes use of co-planarity and projection of point on plane concepts to reduce the localization error. Results have shown that CP-NR algorithm is superior to NR algorithm and comparison is done for the localization accuracy with respect to variations in range, anchor density and node density.</p>

scholarly journals Species and characteristics of volatile organic compounds emitted from an auto-repair painting workshop

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Y. Song ◽  
H. Chun
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Large Scale ◽  
Butyl Acetate ◽  
Small Scale ◽  
Indoor Environments ◽  
Voc Emissions ◽  
Volatile Organic ◽  
Before And After ◽  
Adsorption Systems

AbstractVolatile organic compounds (VOCs) are secondary pollutant precursors having adverse impacts on the environment and human health. Although VOC emissions, their sources, and impacts have been investigated, the focus has been on large-scale industrial sources or indoor environments; studies on relatively small-scale enterprises (e.g., auto-repair workshops) are lacking. Here, we performed field VOC measurements for an auto-repair painting facility in Korea and analyzed the characteristics of VOCs emitted from the main painting workshop (top coat). The total VOC concentration was 5069–8058 ppb, and 24–35 species were detected. The VOCs were mainly identified as butyl acetate, toluene, ethylbenzene, and xylene compounds. VOC characteristics differed depending on the paint type. Butyl acetate had the highest concentration in both water- and oil-based paints; however, its concentration and proportion were higher in the former (3256 ppb, 65.5%) than in the latter (2449 ppb, 31.1%). Comparing VOC concentration before and after passing through adsorption systems, concentrations of most VOCs were lower at the outlets than the inlets of the adsorption systems, but were found to be high at the outlets in some workshops. These results provide a theoretical basis for developing effective VOC control systems and managing VOC emissions from auto-repair painting workshops.

scholarly journals Realized Applications of Positioning Technologies in Defense Intelligence

2008 ◽  
pp. 1975-1993
Author(s):  
Katina Michael ◽  
Amelia Masters
Keyword(s):  
Homeland Security ◽  
Radio Frequency Identification ◽  
Large Scale ◽  
Mobile Telephone ◽  
Small Scale ◽  
Data Networks ◽  
Public And Private ◽  
Key Factor ◽  
Frequency Identification ◽  
Defense Intelligence

Spurred by the recent escalation of terrorist attacks and their increasingly devastating outcomes, defense intelligence in the context of homeland security has been drawn into the spotlight. The challenge, at both national and global levels, of managing information in order to offensively resist attack or defensively keep citizens safe from further harm has never been greater. In meeting this challenge, the tools and strategies used by relevant defensive powers are a key factor in the success or failure of all activities, ranging from small-scale, homeland security administration through to large-scale, all-inclusive war. In all areas within this wide scope, the adoption of positioning technologies has played an important role. Of special significance are the global positioning system (GPS), second-generation (2G) and beyond mobile telephone networks (includingwireless data networks), radio-frequency identification (RFID) and geographic information systems (GIS). Since most positioning technology has been developed or released for use within the commercial sector, however, concerns beyond technological feasibility are raised when applications are harnessed solely for defense. The integration between commercial and military sectors and public and private needs must be considered and, primarily, this involves ensuring that the enforcement of homeland security does not compromise citizen rights.

Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters

2014 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Benjamin Sanda ◽  
Ikhlas Abdel-Qader ◽  
Abiola Akanmu
Keyword(s):  
Real Time ◽  
Radio Frequency Identification ◽  
Kalman Filters ◽  
Tracking Error ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Real World Data ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Localization Systems

The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.

scholarly journals A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
Author(s):  
Xiaochao Dang ◽  
Xiong Si ◽  
Zhanjun Hao ◽  
Yaning Huang
Keyword(s):  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Signal Interference ◽  
Indoor Environments ◽  
Phase Information ◽  
Localization Accuracy ◽  
Localization Method ◽  
Indoor Location ◽  
Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

scholarly journals MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1645 ◽  
Author(s):  
Ryota Kimoto ◽  
Shigemi Ishida ◽  
Takahiro Yamamoto ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Single Channel ◽  
Sensor Nodes ◽  
Localization Problem ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Localization System ◽  
Channel Diversity ◽  
Radio Signals

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

Improved SfM-Based Indoor Localization with Occlusion Removal

2018 ◽  
Vol 10 (3) ◽  
pp. 24-40
Author(s):  
Yushi Li ◽  
George Baciu ◽  
Yu Han ◽  
Chenhui Li
Keyword(s):  
Indoor Localization ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Data Sets ◽  
Indoor Environments ◽  
Decomposition Approach ◽  
Localization Accuracy ◽  
Occlusion Removal ◽  
Accuracy Increase

This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.

scholarly journals Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 439 ◽  
Author(s):  
Shengxin Xu ◽  
Heng Liu ◽  
Fei Gao ◽  
Zhenghuan Wang
Keyword(s):  
Multipath Fading ◽  
Spatial Diversity ◽  
Tomographic Imaging ◽  
Link Quality ◽  
Small Scale ◽  
Reconstruction Method ◽  
Indoor Environments ◽  
Localization Accuracy ◽  
Measurement Variation ◽  
Radio Tomographic Imaging

Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance.

scholarly journals An efficient crowd-sourcing-based approach for fingerprint database updating

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985851
Author(s):  
Xi Liu ◽  
Jian Cen ◽  
Yiju Zhan ◽  
Chengpei Tang
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Primary Concern ◽  
Indoor Environments ◽  
Crowd Sourcing ◽  
Static Data ◽  
Decision Methods ◽  
Fingerprint Database ◽  
Update Frequency

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

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