Determining the best location for indoor access point based on received signal strength

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

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Indoor location using received signal strength of IEEE 802.11b access point

Canadian Conference on Electrical and Computer Engineering, 2005. ◽

10.1109/ccece.2005.1557232 ◽

2006 ◽

Cited By ~ 19

Author(s):

G.I. Wassi ◽

C. Despins ◽

D. Grenier ◽

C. Nerguizian

Keyword(s):

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Ieee 802.11B ◽

Indoor Location

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Research and Application of Underground WLAN Adaptive Radio Fingerprint Database

Sensors ◽

10.3390/s20041182 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1182

Author(s):

Jiansheng Qian ◽

Mingzhi Song

Keyword(s):

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Reference Points ◽

Normal Operation ◽

Positioning System ◽

Widespread Application ◽

Radio Fingerprint ◽

Fingerprint Database ◽

User Location

Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious and time-consuming process. When the underground environments change, it may be necessary to update the signal received signal strength indication (RSSI) of all reference points, which will affect the normal working of a personnel positioning system. To solve this problem, an adaptive construction and update method based on a quantum-behaved particle swarm optimization–user-location trajectory feedback (QPSO–ULTF) for a radio fingerprint database is proposed. The principle of ULTF is that the mobile terminal records and uploads the related dataset in the process of user’s walking, and it forms the user-location track with RSSI through the analysis and processing of the positioning system server. QPSO algorithm is used for the optimal radio fingerprint match between the RSSI of the access point (AP) contained in the dataset of user-location track and the calibration samples to achieve the adaptive generation and update of the radio fingerprint samples. The experimental results show that the radio fingerprint database generated by the QPSO–ULTF is similar to the traditional radio fingerprint database in the statistical distribution characteristics of the signal received signal strength (RSS) at each reference point. Therefore, the adaptive radio fingerprint database can replace the traditional radio fingerprint database. The comparable results of well-known traditional positioning methods demonstrate that the radio fingerprint database generated or updated by the QPSO–ULTF has a good positioning effect, which can ensure the normal operation of a personnel positioning system.

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PEOPLE’S PPRESENCE EFFECT ON WLAN-BASED IPS’ ACCURACY

Jurnal Teknologi ◽

10.11113/jt.v77.6203 ◽

2015 ◽

Vol 77 (9) ◽

Cited By ~ 2

Author(s):

Iyad H Alshami ◽

Noor Azurati Ahmad ◽

Shamsul Sahibuddin

Keyword(s):

Low Cost ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Positioning System ◽

Indoor Environments ◽

Received Signal Strength Indicator ◽

Indoor Positioning System ◽

Closed Environment ◽

Presence Of People

In order to enable Location Based Service (LBS) closed environment, many technologies have been investigated to replace the Global Positioning System (GPS) in the localization process in indoor environments. WLAN is considered as the most suitable and powerful technology for Indoor Positioning System (IPS) due to its widespread coverage and low cost. Although WLAN Received Signal Strength Indicator (RSS) fingerprinting can be considered as the most accurate IPS method, this accuracy can be weakened due to WLAN RSS fluctuation. WLAN RSS fluctuates due to the multipath being influenced by obstacles presence. People presence under WLAN coverage can be considered as one of the main obstacles which can affect the WLAN-IPS accuracy. This research presents experimental results demonstrating that people’s presence between access point (AP) and mobile device (MD) reduces the received signal strength by -2dBm to -5dBm. This reduction in RSS can lead to distance error greater than or equal to 2m. Hence, any accurate IPS must consider the presence of people in the indoor environment.

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Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Sensors ◽

10.3390/s20071828 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1828

Author(s):

Marcin Luckner ◽

Rafał Górak

Keyword(s):

Quality Of Service ◽

Dynamic System ◽

Automatic Detection ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Strength Characteristics ◽

The Mean ◽

Indoor Localisation

This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.

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Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

Remote Sensing ◽

10.3390/rs12121995 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1995

Author(s):

David Sánchez-Rodríguez ◽

Miguel A. Quintana-Suárez ◽

Itziar Alonso-González ◽

Carlos Ley-Bosch ◽

Javier J. Sánchez-Medina

Keyword(s):

Channel State Information ◽

Indoor Localization ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Location Estimation ◽

Channel State ◽

State Information ◽

Single Access ◽

5 Ghz

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

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COMPARISON OF WIRELESS ADAPTERS INTERFERENCE BASED ON DIFFERENCES OF FLOOR POSITION

Jurnal Teknologi ◽

10.11113/jurnalteknologi.v83.16406 ◽

2021 ◽

Vol 83 (4) ◽

pp. 151-157

Author(s):

Sasa Ani Arnomo ◽

Yulia Yulia ◽

Noraini Ibrahim

Keyword(s):

Wireless Network ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Weak Signal ◽

Received Signal Strength Indicator ◽

Average Value ◽

Internet Users ◽

Wireless Signal

The wireless network adapter has now been widely developed. The wireless adapter receives signal strength has several levels. It depends on the signal strength of the device with respect to the wireless signal and access point. The external adapter is used by computers that are not equipped with a wireless network adapter. Usually, internet users use a USB wireless adapter. An external adapter can also amplify signal reception from hotspots. The problem is how users consider using an external wireless adapter or just an onboard wireless adapter when there are many networks interfering with the weak signal strength of the access point. The method is implemented by measuring the value of the Received Signal Strength Indicator (RSSI). The purpose of this study is to compare the received signal strength in conditions where the signal before interference and after experiencing interference (interference). Meanwhile, the measured RSSI value is divided into four floors and with different distances for each floor. Each floor is measured by five distances. The average value of signal strength at a distance of 10 meters is -74 dBm using the onboard wireless adapter and -69 dBm using a USB wireless adapter. The value obtained after the interference is -75 dBm and the USB wireless adapter gets -61 dBm. The reduction of the RSSI value between interference and non-interference is 3%. The onboard wireless adapter is affected by interference while the external wireless adapter is not affected.

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PRAPD: A novel received signal strength–based approach for practical rogue access point detection

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718795838 ◽

2018 ◽

Vol 14 (8) ◽

pp. 155014771879583 ◽

Cited By ~ 4

Author(s):

Wenjia Wu ◽

Xiaolin Gu ◽

Kai Dong ◽

Xiaomin Shi ◽

Ming Yang

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Missing Values ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Local Networks ◽

Strength Based ◽

Point Detection ◽

Rogue Access Point

Rogue access point attack is one of the most important security threats for wireless local networks and has attracted great attention from both academia and industry. Utilizing received signal strength information is an effective solution to detect rogue access points. However, the received signal strength information is formed by multi-dimensional received signal strength vectors that are collected by multiple sniffers, and these received signal strength vectors are inevitably lacking in some dimensions due to the limited wireless transmission range and link instability. This will result in high false alarm rate for rogue access point detection. To solve this issue, we propose a received signal strength–based practical rogue access point detection approach, considering missing received signal strength values in received signal strength vectors collected in practical environment. First, we present a preprocessing scheme for received signal strength vectors, eliminating missing values by means of data filling, filtering, and averaging. Then, we perform clustering analysis on the received signal strength vectors, where we design a distance measurement method that dynamically uses partial components in received signal strength vectors to minimize the distance deviation due to missing values. Finally, we conduct the experiments to evaluate the performance of the practical rogue access point detection. The results demonstrate that the practical rogue access point detection can significantly reduce the false alarm rate while ensuring a high detection rate.

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Indoor localization system based on virtual access points with filtering schemes

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719866135 ◽

2019 ◽

Vol 15 (7) ◽

pp. 155014771986613 ◽

Cited By ~ 2

Author(s):

Dong Myung Lee ◽

Boney Labinghisa

Keyword(s):

Kalman Filter ◽

Indoor Environment ◽

Performance Testing ◽

Signal Strength ◽

Access Point ◽

Received Signal Strength ◽

Indoor Environments ◽

Received Signal Strength Indicator ◽

Access Points ◽

Error Distance

In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

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Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning

Electronics ◽

10.3390/electronics9060982 ◽

2020 ◽

Vol 9 (6) ◽

pp. 982 ◽

Cited By ~ 1

Author(s):

Yue Liu ◽

Rashmi Sharan Sinha ◽

Shu-Zhi Liu ◽

Seung-Hoon Hwang

Keyword(s):

Deep Learning ◽

Side Information ◽

Success Probability ◽

Dynamic Environment ◽

Signal Strength ◽

Access Point ◽

Indoor Positioning ◽

Received Signal Strength ◽

Learning Classifier ◽

Learning Classifiers

Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning.

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