A Practical Indoor Positioning System Based on Collaborative PDR and Wi-Fi Fingerprinting

An Indoor Positioning System (IPS) is vital in pro-viding location-based service (LBS) indoors. The shortcomings of the Global Positioning System (GPS) indoors have led to the development of many alternative solutions for IPS. Wi-Fi finger-printing is a promising solution due to its ability to accurately predict indoor locations. Moreover, the widespread deployment of Wi-Fi infrastructures makes the Wi-Fi fingerprinting-based IPS technique readily applicable. However, this technique re-quires high overhead due to the need to define many reference points (RPs) within the subject area. Multiple Wi-Fi received signal strength (RSS) samples shall be collected from each RP, introducing extensive manual work. This fact makes the Wi-Fi fingerprinting-based IPS is less practical. This issue is addressed by employing pedestrian dead reckoning (PDR) as part of the proposed technique. Realizing a practical IPS based on collaborative use of PDR and Wi-Fi fingerprinting. In the proposed method, most of the time, the location prediction will be based on PDR. When the step count reaches a threshold, a switch from PDR to Wi-Fi fingerprinting is made to decide the user's location. Then, the system resets the step count and uses the PDR again. This approach significantly reduces the total number of RPs needed by the system. The numerical results show that the proposed technique has 99% lower overhead than the conventional Wi-Fi fingerprinting technique and achieves 55.6% better prediction accuracy than the standalone PDR technique. The results justify the benefits of the proposed IPS based on collaborative PDR and Wi-Fi fingerprinting.

Authors

Institutions

Publication Details

Published
2024-12-17
DOI
https://doi.org/10.1109/icicyta64807.2024.10913288
Citations
1
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Practical Indoor Positioning System Based on Collaborative PDR and Wi-Fi Fingerprinting

Muhammad Syahrul Asyraf, Siva Priya Thiagarajah, Made Harta Dwijaksara, Raissa Tito Safaraz et al.
1 citations
Indoor and Outdoor Localization Technologies
0.21
article

A Practical Indoor Positioning System Based on Collaborative PDR and Wi-Fi Fingerprinting

Muhammad Syahrul Asyraf, Siva Priya Thiagarajah, Made Harta Dwijaksara, Raissa Tito Safaraz, Julius Prayoga Raka Nugroho
article en
1 citations

Abstract

An Indoor Positioning System (IPS) is vital in pro-viding location-based service (LBS) indoors. The shortcomings of the Global Positioning System (GPS) indoors have led to the development of many alternative solutions for IPS. Wi-Fi finger-printing is a promising solution due to its ability to accurately predict indoor locations. Moreover, the widespread deployment of Wi-Fi infrastructures makes the Wi-Fi fingerprinting-based IPS technique readily applicable. However, this technique re-quires high overhead due to the need to define many reference points (RPs) within the subject area. Multiple Wi-Fi received signal strength (RSS) samples shall be collected from each RP, introducing extensive manual work. This fact makes the Wi-Fi fingerprinting-based IPS is less practical. This issue is addressed by employing pedestrian dead reckoning (PDR) as part of the proposed technique. Realizing a practical IPS based on collaborative use of PDR and Wi-Fi fingerprinting. In the proposed method, most of the time, the location prediction will be based on PDR. When the step count reaches a threshold, a switch from PDR to Wi-Fi fingerprinting is made to decide the user's location. Then, the system resets the step count and uses the PDR again. This approach significantly reduces the total number of RPs needed by the system. The numerical results show that the proposed technique has 99% lower overhead than the conventional Wi-Fi fingerprinting technique and achieves 55.6% better prediction accuracy than the standalone PDR technique. The results justify the benefits of the proposed IPS based on collaborative PDR and Wi-Fi fingerprinting.

Multimedia University (MY), University of Indonesia (ID)
Industry, innovation and infrastructure
Openalex Percentile: Top 43%
Indoor and Outdoor Localization Technologies
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.