Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020527" target="_blank" >RIV/62690094:18450/23:50020527 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10145106" target="_blank" >https://ieeexplore.ieee.org/document/10145106</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3283592" target="_blank" >10.1109/ACCESS.2023.3283592</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

Popis výsledku v původním jazyce

The performance of fingerprint-based indoor wireless localization systems (IWL-Ss) can be enhanced using fingerprint clustering. The localization performance of clustered fingerprint-based IWL-Ss is affected by several factors, including choosing the most optimal initial parameters and the appropriate fingerprint similarity measurement metric. The problem of choosing the best initial parameter is solved by using the affinity propagation clustering (APC) algorithm in this paper, which automatically calculates the number of clusters and cluster centroid vectors. However, the choice of fingerprint similarity measure and the selection of the best cluster centroid when there are multiple potential cluster centroids limit the performance of the APC algorithm. To address this issue, this paper proposes modifying the conventional APC (c-APC) algorithm, which will be referred to as the &quot;m-APC algorithm.&quot; The context similarity coefficient (CSC) fingerprint similarity measure replaces the distance-based fingerprint similarity measure used by the c-APC algorithm. Furthermore, the cluster centroids that are generated automatically are replaced by the centroid that is obtained by averaging all fingerprints within a cluster. Using the k-NN localization algorithm and four online fingerprint databases, the performance of the m-APC+CSC algorithm is determined and compared to the c-APC algorithm using cosine, Euclidean, and Shepard distances as fingerprint similarity measures. Based on simulation results, the m-APC algorithm reduced the position root mean square error (RMSE) and mean absolute error (MAE) by about 12% and 8%, respectively, when compared to the c-APC algorithm when both used the CSC as a fingerprint similarity measure. Furthermore, the m-APC+CSC algorithm achieved an 8% and 9%, respectively, position RMSE and MAE reduction over the c-APC algorithm using cosine, Euclidean, and Shepard distances as similarity measurements. The m-APC+CSC algorithm should, however, be used on a reasonably sized fingerprint database with at least four wireless access points (APs) for better localization performance.

Název v anglickém jazyce

Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

Popis výsledku anglicky

The performance of fingerprint-based indoor wireless localization systems (IWL-Ss) can be enhanced using fingerprint clustering. The localization performance of clustered fingerprint-based IWL-Ss is affected by several factors, including choosing the most optimal initial parameters and the appropriate fingerprint similarity measurement metric. The problem of choosing the best initial parameter is solved by using the affinity propagation clustering (APC) algorithm in this paper, which automatically calculates the number of clusters and cluster centroid vectors. However, the choice of fingerprint similarity measure and the selection of the best cluster centroid when there are multiple potential cluster centroids limit the performance of the APC algorithm. To address this issue, this paper proposes modifying the conventional APC (c-APC) algorithm, which will be referred to as the &quot;m-APC algorithm.&quot; The context similarity coefficient (CSC) fingerprint similarity measure replaces the distance-based fingerprint similarity measure used by the c-APC algorithm. Furthermore, the cluster centroids that are generated automatically are replaced by the centroid that is obtained by averaging all fingerprints within a cluster. Using the k-NN localization algorithm and four online fingerprint databases, the performance of the m-APC+CSC algorithm is determined and compared to the c-APC algorithm using cosine, Euclidean, and Shepard distances as fingerprint similarity measures. Based on simulation results, the m-APC algorithm reduced the position root mean square error (RMSE) and mean absolute error (MAE) by about 12% and 8%, respectively, when compared to the c-APC algorithm when both used the CSC as a fingerprint similarity measure. Furthermore, the m-APC+CSC algorithm achieved an 8% and 9%, respectively, position RMSE and MAE reduction over the c-APC algorithm using cosine, Euclidean, and Shepard distances as similarity measurements. The m-APC+CSC algorithm should, however, be used on a reasonably sized fingerprint database with at least four wireless access points (APs) for better localization performance.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

57341-57348

Kód UT WoS článku

001012224100001

EID výsledku v databázi Scopus

2-s2.0-85161568489