scholarly journals Factor Optimization for the Design of Indoor Positioning Systems Using a Probability-Based Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 16
Author(s):  
Bráulio Henrique O. U. V. Pinto ◽  
Horácio A. B. F. de Oliveira ◽  
Eduardo J. P. Souto
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Indoor Positioning ◽  
Test Point ◽  
Reference Points ◽  
Location Based Services ◽  
Area Network ◽  
Positioning Systems ◽  
Positioning Algorithm ◽  
The Impact

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They advertise useful information, such as the received signal strength (RSS), that is processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing the arrangement of APs over the environment using an enhanced probability-based algorithm. From the assumption that a log-distance path loss model can reasonably describe, on average, the distribution of RSS throughout the environment, we build a simulation framework to analyze the impact, on the accuracy, of the main factors that constitute the positioning algorithm, such as the number of reference points (RPs) and the number of samples of RSS collected per test point. To demonstrate the applicability of the proposed solution, a real-world testbed dataset is used for validation. The obtained results for accuracy show that the trends verified via simulation strongly correlate to the verified in the dataset processing when allied with an optimal configuration of APs. This indicates our method is capable of providing an optimal factor combination—through early simulations—for the design of more efficient IPSs that rely on a probability-based positioning algorithm.

scholarly journals A Probabilistic Approach for Optimal Design of Indoor Positioning Systems

2020 ◽  
Author(s):  
Bráulio Henrique O. U. V. Pinto ◽  
Horácio A. B. F. de Oliveira ◽  
Eduardo Souto
Keyword(s):  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Location Based Services ◽  
Design Parameters ◽  
Area Network ◽  
Localization Error ◽  
Positioning Systems ◽  
Access Points ◽  
The Impact

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

scholarly journals Multidimensional Optimization of Signal Space Distance Parameters in WLAN Positioning

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Milenko Brković ◽  
Mirjana Simić
Keyword(s):  
High Speed ◽  
Wireless Local Area Network ◽  
Nearest Neighbor ◽  
Local Area Network ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Area Network ◽  
Signal Space ◽  
Indoor Location ◽  
Space Distance

Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware.

scholarly journals Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanfen Le ◽  
Shijialuo Jin ◽  
Hena Zhang ◽  
Weibin Shi ◽  
Heng Yao
Keyword(s):  
Ridge Regression ◽  
Local Area Network ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Feature Reduction ◽  
Reference Points ◽  
Area Network ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Positioning Method

An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel-based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.

scholarly journals A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

2017 ◽  
Vol 29 (3) ◽  
Author(s):  
Wilson Sakpere ◽  
Michael Adeyeye Oshin ◽  
Nhlanhla BW Mlitwa
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
State Of The Art ◽  
Indoor Positioning ◽  
Ultra Wideband ◽  
Area Network ◽  
Navigation Systems ◽  
Positioning Systems

The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

scholarly journals Performance Analysis of Wireless Local Area Network for a High-/Low-Priority Traffic Ratio at Different Numbers of Access Categories

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 693
Author(s):  
Kvitoslava Obelovska ◽  
Olga Panova ◽  
Vincent Karovič
Keyword(s):  
Network Performance ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Area Network ◽  
Large Network ◽  
Medium Access ◽  
Massive Growth ◽  
Adaptive Adjustment ◽  
The Impact

The performance of Wireless Local Area Network (WLAN) is highly dependent on the processes that are implemented in the Medium Access Control (MAC) sublayer regulated by the IEEE 802.11 standard. In turn, various parameters affect the performance of the MAC sublayer, the most important of which is the number of stations in the network and the offered load. With the massive growth of multimedia traffic, research of the network performance depending on traffic types is relevant. In this paper, we present the impact of a high-/low-priority traffic ratio on WLAN performance with different numbers of access categories. The simulation results show different impact of high-/low-priority traffic ratio on the performance of the MAC sublayer of wireless LANs depending on different network-sizes and on network conditions. Performance of the large network with two access categories and with the prevalent high-priority traffic is significantly higher than in the case of using four categories on the MAC sublayer. This allows us to conclude that the performance improvement of the large network with the prevalent high-priority traffic can be achieved by an adaptive adjustment of the access categories number on the MAC sublayer.

Indoor Positioning in WLAN Environment Based on Support Vector Regression and Space Partitioning

2011 ◽  
Vol 204-210 ◽  
pp. 1599-1602 ◽  
Author(s):  
Zhi An Deng ◽  
Yu Bin Xu ◽  
Di Wu
Keyword(s):  
Support Vector Regression ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Physical Space ◽  
Mapping Function ◽  
Local Area ◽  
Indoor Positioning ◽  
Support Vector ◽  
Area Network ◽  
Space Partitioning

Indoor positioning system in wireless local area network (WLAN) has been a subject of intensive research due to its cost effectiveness and reasonable positioning accuracy. A new WLAN indoor positioning algorithm based on support vector regression (SVR) and space partitioning is proposed. The whole positioning environment is partitioned into several subspaces by combining k-means clustering method and binary support vector classifiers (SVC). Then the mapping function between received signal strength (RSS) and the physical space is established by SVR machine for each subspace. Subspace with much smaller physical range means more compact input feature space and leads to the enhancement of generalization capability for each SVR machine. The proposed algorithm and other well-known positioning algorithms are carried and compared in a real WLAN environment. Experimental results show that the proposed algorithm achieves 14.6 percent (0.31m) improvement than the single SVR algorithm in the sense of mean positioning error.

Analysis of electromagnetic absorption in new design of PIFA antenna using metamaterials

2021 ◽  
Author(s):  
Hamza Ben Hamadi ◽  
said ghnimi ◽  
Lassaad Latrach ◽  
Philippe Benech ◽  
Ali Gharsallah
Keyword(s):  
Human Body ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Dual Band ◽  
Area Network ◽  
Dual Band Antenna ◽  
Cubic Model ◽  
The Impact ◽  
Pifa Antenna

Abstract This paper presents the design, simulation and fabrication of a miniaturized wearable dual-band antenna on a semi-flex substrate; she is operable at 2.45/5.8 GHz for wireless local area network applications. The electrical and radiation characteristics of this proposed antenna were obtained by means of a technical of insertion of a slot to tune the operating frequencies. To study the impact of the electromagnetic radiation of the structure of the human body, it is necessary to minimize the back radiation towards the user. Therefore, in this work, a multi-band artificial magnetic conductor (AMC) was placed directly above a dual-band planar inverted F antenna to achieve a miniaturization with excellent radiation performance. The simulation results were designed and simulated using Studio commercial software (CST). A good agreement was achieved between the results of simulation and the experimental. The Comparison of measurement results indicates that the gain improved from 1,84 dB to 3,8 dB, in the lower band, and from 2,4 dB to 4,1 in the upper band, when the antenna is backed by the AMC plane. The front-to-back ratio of the AMC backed PIFA antenna was also enhanced. Then, to ensure that the proposed AMC is harmless to the human body, this prototype was placed on three-layer human tissue cubic model. It was observed that the through inclusion of plane AMC, the peak specific absorption rate (SAR) decreased to 1,45 and 1,1 W/kg at 2,45 and 5.8 GHz, respectively (a reduction of around 3,7 W/kg, compared with an antenna without (AMC).

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