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.

Survey of WLAN Fingerprinting Positioning System

2013 ◽  
Vol 380-384 ◽  
pp. 2499-2505 ◽  
Author(s):  
Ping Chen ◽  
Yu Bin Xu ◽  
Liang Chen ◽  
Zhi An Deng
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Optimization Techniques ◽  
Location Estimation ◽  
Location Based Services ◽  
Area Network ◽  
Positioning System ◽  
Indoor Location ◽  
Location Clustering

The increasing demand for indoor location based services has motivated the development of various indoor positioning methods. Among them, fingerprinting based positioning system in wireless local area network (WLAN) has been paid more attentions due to its cost effectiveness and relatively high accuracy. This paper investigates various optimization techniques for WLAN fingerprinting positioning comprehensively. The fingerprinting positioning comprises five major steps: Radio Map construction, location clustering, feature extraction, location estimation and tracking. The optimization techniques in these five steps are discussed and finally the future trends are presented.

An Effective Adaptive Autocorrelation-Based Neighboring Matching Location-Aware Computing in WLANs

2011 ◽  
Vol 204-210 ◽  
pp. 2011-2014
Author(s):  
Yu Bin Xu ◽  
Mu Zhou ◽  
Lin Ma
Keyword(s):  
High Speed ◽  
Wireless Local Area Network ◽  
Nearest Neighbor ◽  
Local Area Network ◽  
Local Area ◽  
Measured Signal ◽  
Area Network ◽  
K Nearest Neighbor ◽  
Context Aware Computing ◽  
High Efficient

The recent advances of ubiquitous wireless infrastructures and requirements for high speed context-aware computing have created the opportunities to supply the high efficient location based service (LBS) in indoor wireless local area network (WLAN) environment. Because of the serious multi-path effect, unpredictable co-channel interference and inherent equipment noise, the measured signal strengths vary a lot in the real-world indoor environment. And this strength variation will also result in the performance deterioration of radio map-based neighboring matching algorithm. In response to this compelling problem, we propose the adoption of adaptive autocorrelation-based signal preprocessing method as a specific solution by effectively eliminating the singular strength from the original fingerprint set. Finally, the feasibility and effectiveness of autocorrelation-based preprocessing are also verified by decreasing about 33.4% and 32.9% of errors in k nearest neighbor (KNN) and weighted KNN (WKNN).

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 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.

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.

scholarly journals Indoor Localization within Multi-Story Buildings Using MAC and RSSI Fingerprint Vectors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2433 ◽  
Author(s):  
Litao Han ◽  
Li Jiang ◽  
Qiaoli Kong ◽  
Ji Wang ◽  
Aiguo Zhang ◽  
...  
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Media Access Control ◽  
Area Network ◽  
Media Access ◽  
Novel Approach ◽  
The Difference

For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings.

scholarly journals Highest Cost First-Based QoS Mapping Scheme for Fiber Wireless Architecture

Photonics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 114
Author(s):  
Khalid.H. Mohammadani ◽  
Rizwan Aslam Butt ◽  
Kamran Ali Memon ◽  
Fayaz Hassan ◽  
Abdul Majeed ◽  
...  
Keyword(s):  
High Speed ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Optical Network ◽  
Access Network ◽  
Local Area ◽  
Passive Optical Network ◽  
Area Network ◽  
Broadband Wireless ◽  
Dynamic Bandwidth Assignment

The combination of a high-speed wireless network with passive optical network technologies has led to the evolution of a modern integrated fiber wireless (FiWi) access network. Compared to broadband wireless networks, the FiWi network offers higher bandwidth with improved reliability and reduced maintenance costs due to the passive nature of passive optical network (PON). Since the quality of service (QoS) is a baseline to deploy high-speed FiWi broadband access networks, therefore, it is essential to analyze and reduce the typical problems (e.g., bandwidth and delay) in the high-speed next-generation networks (NGANs). This study investigates the performance of a fiber wireless architecture where a 10-Gigabit-capable passive optical network (XGPON) and fifth generation of wireless local area network (WLAN) (i.e., IEEE 802.11ac) are integrated. Both technologies take benefits from each other and have pros and cons concerning the QoS demands of subscribers. The proposed work offers a very flexible QoS scheme for the different types of services of 5G WLAN and XGPON with the help of the highest cost first (HCF) algorithm, which leads to reduced upstream delays for delay-sensitive applications. The simulation results show that the HCF algorithm boosts the performance of the dynamic bandwidth assignment (DBA) scheme and results in up to 96.1%, 90.8%, and 55.5% reduced upstream (US) delays for video: VI(T2), background: BK(T3), and best effort: BE(T4) traffic in enhanced-distributed-channel-access (EDCA) mode. Compared to earlier work, the HCF and immediate allocation with the colorless grant (IACG) DBA combination results in the reduction of up to 54.8% and 53.4% mean US delays. This happens because of 50% to 65% better bandwidth assignment by the IACG DBA process due to efficient mapping by the HCF algorithm.

Sign in / Sign up

Export Citation Format

Share Document