Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Adaptive Temporal Radio Maps for Indoor Location Estimation
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
An Adaptive Two-Phase Approach to WiFi Location Sensing
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
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Location determination for mobile devices in wireless environment has gained much attention in the past years. For those location-unaware devices, detecting radio frequency signal strength from outdoor Base Station or indoor Access Point is the most popular method. However, traditional radio map matching scheme suffers from variation and error when the captured wireless signals significantly deviate from the measured samples stored in advance. To solve this problem, a neighbor-assisted location calibration mechanism with two-phase approach is proposed. Firstly, mobile terminal utilizes conventional pattern mapping to identify its general location; if the result includes some inconclusive fuzzy area, location information from its short distance neighbors will be retrieved as reference to calibrate location estimation in the second step. With the cooperation among mobile devices, a maximum shared border algorithm is designed to compress the fuzzy area, so that the location correction rate is improved and no additional hardware support is required. Such mechanism can be deployed in either outdoor or indoor environment, and the simulation results also show the proposed mechanism has stable performance in scalability under different density distribution of mobile nodes.