ArticleSensors (Basel, Switzerland)2019
Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Effortless 3D radio maps generation for fingerprinting-based indoor positioning system.Scientific reports · 2025Article
- Article
- Some Design Considerations in Passive Indoor Positioning Systems.Sensors (Basel, Switzerland) · 2023Article
- Smartphone Sensors for Indoor Positioning.Sensors (Basel, Switzerland) · 2023Article
- An Indoor Location-Based Augmented Reality Framework.Sensors (Basel, Switzerland) · 2023Article
- Wi-Fi Access Point Design Concept Targeting Indoor Positioning for Smartphones and IoT.Sensors (Basel, Switzerland) · 2022Article
- Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home.Sensors (Basel, Switzerland) · 2022Article
- An RSS Transform-Based WKNN for Indoor Positioning.Sensors (Basel, Switzerland) · 2021Article
- Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System.Sensors (Basel, Switzerland) · 2021Article
- HPIPS: A High-Precision Indoor Pedestrian Positioning System Fusing WiFi-RTT, MEMS, and Map Information.Sensors (Basel, Switzerland) · 2020Article
- Underground Coal Mine Fingerprint Positioning Based on the MA-VAP Method.Sensors (Basel, Switzerland) · 2020Article
- A Novel Position and Orientation Sensor for Indoor Navigation Based on Linear CCDs.Sensors (Basel, Switzerland) · 2020Article
- Indoor Navigation Systems for Visually Impaired Persons: Mapping the Features of Existing Technologies to User Needs.Sensors (Basel, Switzerland) · 2020Review
- DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier.Sensors (Basel, Switzerland) · 2019Article
- LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system's reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user's position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user's current position with the help of APs' intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.