scholarly journals Automatic Detection of Missing Access Points in Indoor Positioning System †

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3595 ◽  
Author(s):  
Rafał Górak ◽  
Marcin Luckner
Keyword(s):  
Automatic Detection ◽  
Significant Loss ◽  
Classification Error ◽  
Positioning System ◽  
Random Forest Algorithm ◽  
Access Points ◽  
The Mean ◽  
Academic Building ◽  
Indoor Positioning System ◽  
Indoor Localisation

The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

scholarly journals Trilateration Method For Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Herryawan Pujiharsono ◽  
Duwi Utami ◽  
Rafina Destiarti Ainul
Keyword(s):  
Path Loss ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Positioning System ◽  
Observation Area ◽  
Current Location ◽  
Zigbee Protocol ◽  
The Mean ◽  
Unknown Node ◽  
Indoor Positioning System

Wireless network technology that is used today is developing rapidly because of the increasing need for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a disadvantage of low accuracy of 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as an object and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate unknown node located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area is 0.9836 and the Mean Square Error of the test is 1.251 meters, which indicates that the system can be a solution to the indoor GPS problem.

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

Sensors ◽  
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.

scholarly journals A Hidden Markov Model to Estimate the Time Dairy Cows Spend in Feeder Based on Indoor Positioning Data

2018 ◽  
Author(s):  
Matti Pastell ◽  
Frondelius Lilli
Keyword(s):  
Dairy Cows ◽  
State Transition ◽  
Viterbi Algorithm ◽  
Transition Probabilities ◽  
Wide Band ◽  
Indoor Positioning ◽  
Feeding Time ◽  
Positioning System ◽  
The Mean ◽  
Indoor Positioning System

The feeding time of dairy cows is linked with the health status of the animal and can be used to estimate daily feed intake together with other measurements. The aim of this study was to develop a model to measure the time a dairy cow spends at a feed bunk using an Ultra wide-band indoor positioning system.We measured the feeding behavior of 50 dairy cows during 7 days using Ubisense indoor positioning system and Insentec roughage feeders. We calculated the feeding (presence at the feeder) probability of the cow using logistic regression model with the distance to feed barrier as input and used the Viterbi algorithm to calculate the most likely state (feeding or not feeding) given state transition probabilities. The model was able to predict whether the cow was at the feeding trough or not with the accuracy of 97.6%, sensitivity 95.3% and specificity 97.9%. The model was also able to estimate the mean bout duration and the number of feeding bouts.

scholarly journals Wi‑Fi Indoor Localisation: A Deeper Insight Into Patterns in the Fingerprint Map Data

2018 ◽  
Vol 66 (6) ◽  
pp. 1565-1571
Author(s):  
Mikuláš Muroň ◽  
David Procházka
Keyword(s):  
Mobile Device ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Map Data ◽  
Almost All ◽  
The University ◽  
Indoor Positioning System ◽  
Indoor Localisation ◽  
Insight Into

Localisation via Wi‑Fi networks is one of the possible techniques which can be used for positioning inside buildings or in other places without the GPS signal. The accurate indoor positioning system can help users with localisation or navigation within unfamiliar places. Almost all buildings are covered with the Wi‑Fi signal. Using the currently existing infrastructure will minimise cost for construction other types of indoor positioning systems. Among other reasons, usage of Wi‑Fi for positioning is also convenient because almost every mobile device has a Wi‑Fi capability and therefore the system can be easily used by everyone. However, an important factor is the precision of such a solution. The article is focused on the evaluation of Wi‑Fi localisation precision within the university grounds.

Indoor Positioning Using FM Radio

2012 ◽  
pp. 199-211
Author(s):  
Andrei Papliatseyeu ◽  
Venet Osmani ◽  
Oscar Mayora
Keyword(s):  
Experimental Study ◽  
High Performance ◽  
Short Range ◽  
Low Cost ◽  
Indoor Positioning ◽  
Positioning System ◽  
Fm Radio ◽  
Indoor Positioning System ◽  
Indoor Localisation

This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors’ experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.

scholarly journals Indoor Positioning System Using Depth Maps and Wireless Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jaime Duque Domingo ◽  
Carlos Cerrada ◽  
Enrique Valero ◽  
J. A. Cerrada
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Sensory Information ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Access Points ◽  
Depth Maps ◽  
Wireless Positioning ◽  
Indoor Positioning System

This work presents a newIndoor Positioning System(IPS) based on the combination ofWiFi Positioning System(WPS) anddepth maps, for estimating the location of people. The combination of both technologies improves the efficiency of existing methods, based uniquely on wireless positioning techniques. While other positioning systems force users to wear special devices, the system proposed in this paper just requires the use ofsmartphones, besides the installation of RGB-D sensors in the sensing area. Furthermore, the system is not intrusive, being not necessary to know people’s identity. The paper exposes the method developed for putting together and exploiting both types of sensory information with positioning purposes: the measurements of the level of the signal received from different access points (APs) of the wireless network and thedepth mapsprovided by the RGB-D cameras. The obtained results show a significant improvement in terms of positioning with respect to common WiFi-based systems.

scholarly journals CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1114 ◽  
Author(s):  
Feng Qin ◽  
Tao Zuo ◽  
Xing Wang
Keyword(s):  
Indoor Positioning ◽  
Location Estimation ◽  
Ease Of Use ◽  
Positioning System ◽  
Transmission Distance ◽  
Positioning Errors ◽  
The Mean ◽  
Validation Set ◽  
Indoor Positioning System ◽  
Wifi Fingerprint

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.

Indoor Positioning Using FM Radio

2010 ◽  
Vol 1 (3) ◽  
pp. 19-31 ◽  
Author(s):  
Andrei Papliatseyeu ◽  
Venet Osmani ◽  
Oscar Mayora
Keyword(s):  
Experimental Study ◽  
High Performance ◽  
Short Range ◽  
Low Cost ◽  
Indoor Positioning ◽  
Positioning System ◽  
Fm Radio ◽  
Indoor Positioning System ◽  
Indoor Localisation

This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors’ experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.

scholarly journals An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

2020 ◽  
Vol 10 (1) ◽  
pp. 117-123
Author(s):  
Bhulakshmi Bonthu ◽  
M Subaji
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Positioning System ◽  
Radio Waves ◽  
Direct Path ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Measuring Techniques ◽  
Indoor Positioning System

AbstractIndoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.

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