Bayesian Based Location Estimation System Using Wireless LAN
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Design of indoor positioning systems based on location fingerprinting technique
Design of indoor positioning systems based on location fingerprinting technique
ARIADNE: a dynamic indoor signal map construction and localization system
Proceedings of the 4th international conference on Mobile systems, applications and services
IEEE Transactions on Knowledge and Data Engineering
Fuzzy location and tracking on wireless networks
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
Reducing the Calibration Effort for Probabilistic Indoor Location Estimation
IEEE Transactions on Mobile Computing
EURASIP Journal on Applied Signal Processing
Sensor Measurements for Wi-Fi Location with Emphasis on Time-of-Arrival Ranging
IEEE Transactions on Mobile Computing
Expert Systems with Applications: An International Journal
Analysis of WLAN's received signal strength indication for indoor location fingerprinting
Pervasive and Mobile Computing
Advanced support vector machines for 802.11 indoor location
Signal Processing
Outdoor exit detection using combined techniques to increase GPS efficiency
Expert Systems with Applications: An International Journal
Multi-agent location system in wireless networks
Expert Systems with Applications: An International Journal
Wireless Personal Communications: An International Journal
Theoretical entropy assessment of fingerprint-based Wi-Fi localization accuracy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, we propose a three-phase methodology (measurement, calibration and estimation) for locating mobile stations (MS) in an indoor environment using wireless technology. Our solution is a fingerprint-based positioning system that overcomes the problem of the relative effect of doors and walls on signal strength and is independent of network device manufacturers. In the measurement phase, our system collects received signal strength indicator (RSSI) measurements from multiple access points. In the calibration phase, our system utilizes these measurements in a normalization process to create a radio map, a database of RSS patterns. Unlike traditional radio map-based methods, our methodology normalizes RSS measurements collected at different locations (on a floor) and uses artificial neural network models (ANNs) to group them into clusters. In the third phase, we use data mining techniques (clustering) to optimize location results. Experimental results demonstrate the accuracy of the proposed method. From these results it is clear that the system is highly likely to be able to locate a MS in a room or nearby room.