AP Selection Algorithm in WLAN Indoor Localization

Zhou, Yingjun; Chen, Xihan; Zeng, Shulei; Jingzhi, Liu; Dong, Liang

doi:10.3923/itj.2013.3773.3776

Cited by 9 publications

(12 citation statements)

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“…The distributions f i j (r i j = v|p j ) and f i j (r i j = v) are estimated analytically, through histograms, or using kernels. A modification that ranks APs jointly by InfoGain and mutual correlation has also been introduced [117].…”

Section: ) Information Gain (Infogain)mentioning

confidence: 99%

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Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Khalajmehrabadi

Gatsis

Akopian

2017

IEEE Commun. Surv. Tutorials

302

176

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Abstract-Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.

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Section: ) Information Gain (Infogain)mentioning

confidence: 99%

“…8) Joint Selection: This method is similar to Fisher criterion. However, instead of computing the differentiability of RPs with respect to the mean RSS valueψ i , the mutual differentiability between RPs is computed [117], [118]:…”

Section: ) Group Discrimination (Gd)mentioning

confidence: 99%

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Khalajmehrabadi

Gatsis

Akopian

2017

IEEE Commun. Surv. Tutorials

302

176

View full text Add to dashboard Cite

show abstract

“…In order to solve the challenges caused by a huge amount of available data, AP selection criteria have gained a lot of interest within the last few years [ 23 , 24 , 25 , 26 , 28 , 29 ]. Some studies choose to compress AP information via different methods instead of selecting AP subsets, see e.g., [ 17 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…Some studies choose to compress AP information via different methods instead of selecting AP subsets, see e.g., [ 17 , 23 , 24 ]. AP selection can be done either in the online positioning phase [ 24 , 25 , 27 ], in the offline training phase [ 4 , 26 , 30 ], or to take into account both phases [ 21 , 28 ]. In [ 28 ], a discrimination index is calculated for each AP in the offline phase, but the selection is performed only in the online phase, based both on discrimination index and RSS.…”

Section: Related Workmentioning

confidence: 99%

“…The purpose of our paper is to make the choice between different positioning algorithms and characteristics easier, since this kind of comprehensive studies are missing. AP selection has been previously studied also in [ 4 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ], but grid size has not been addressed before in this concept. We will also summarize the findings of a suitable AP selection criteria shown in our previous publications [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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On the Choice of Access Point Selection Criterion and Other Position Estimation Characteristics for WLAN-Based Indoor Positioning

Laitinen

Lohan

2016

Sensors

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The positioning based on Wireless Local Area Networks (WLAN) is one of the most promising technologies for indoor location-based services, generally using the information carried by Received Signal Strengths (RSS). One challenge, however, is the huge amount of data in the radiomap database due to the enormous number of hearable Access Points (AP) that could make the positioning system very complex. This paper concentrates on WLAN-based indoor location by comparing fingerprinting, path loss and weighted centroid based positioning approaches in terms of complexity and performance and studying the effects of grid size and AP reduction with several choices for appropriate selection criterion. All results are based on real field measurements in three multi-floor buildings. We validate our earlier findings concerning several different AP selection criteria and conclude that the best results are obtained with a maximum RSS-based criterion, which also proved to be the most consistent among the different investigated approaches. We show that the weighted centroid based low-complexity method is very sensitive to AP reduction, while the path loss-based method is also very robust to high percentage removals. Indeed, for fingerprinting, 50% of the APs can be removed safely with a properly chosen removal criterion without increasing the positioning error much.

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A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives

Roy

Chowdhury

2022

CCF Trans. Pervasive Comp. Interact.

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Indoor localization has the capability to change the way of providing location-based services in a closed environment and has more potential than that of GPS if the present shortcomings can be overcome. Thus, developing a ubiquitous Indoor Localization System (ILS) is the need of the day. WiFi-based indoor localization using smartphones is a promising approach to achieve ubiquity since smartphones are widely available today and most of the buildings are WiFi enabled. However, the significant variation of WiFi signal strengths with ambient conditions as well as device configuration badly affects the localization accuracy. Hence, a ubiquitous ILS, which would be widely available and would not require any specialized hardware support is still out of reach. Thus, this paper surveys state-of-the-art WiFi-based indoor localization techniques with a critical analysis of their applicability as a ubiquitous system. Consequently, the main objective of this paper is to highlight the key research challenges of implementing a WiFi-based ILS when system ubiquity is the prime concern with a discussion on future direction. Some of the experiments have been implemented on a real-life dataset in order to indicate the implementation challenges of such a system.

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AP Selection Algorithm in WLAN Indoor Localization

Cited by 9 publications

References 0 publications

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

On the Choice of Access Point Selection Criterion and Other Position Estimation Characteristics for WLAN-Based Indoor Positioning

A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives

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