Improved Indoor Localization Performance using a Modified Affinity Propagation Clustering Algorithm with Context Similarity Coefficient

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 appropria...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access Vol. 11; p. 1
Main Authors: Yaro, Abdulmalik Shehu, Maly, Filip, Maly, Karel
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Affinity
Algorithms
APC algorithm
Behavioral sciences
Centroids
Clustering
Clustering algorithms
Context
context similarity coefficient
Euclidean distance
fingerprint
Fingerprint recognition
Fingerprints
k-NN
Localization
Location awareness
Mathematical analysis
Measurement
Parameters
Position measurement
position RMSE
Propagation
Root-mean-square errors
RSS
Similarity measures
Wireless communication systems
ISSN:2169-3536, 2169-3536
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 "m-APC algorithm." 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3283592