Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
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
Wireless LAN location-sensing for security applications
WiSe '03 Proceedings of the 2nd ACM workshop on Wireless security
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Error characteristics and calibration-free techniques for wireless LAN-based location estimation
Proceedings of the second international workshop on Mobility management & wireless access protocols
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Calibration-free WLAN location system based on dynamic mapping of signal strength
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
The Horus location determination system
Wireless Networks
Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization
Mobile Networks and Applications
Adaptive localization techniques in WiFi environments
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Centaur: locating devices in an office environment
Proceedings of the 18th annual international conference on Mobile computing and networking
Zee: zero-effort crowdsourcing for indoor localization
Proceedings of the 18th annual international conference on Mobile computing and networking
Push the limit of WiFi based localization for smartphones
Proceedings of the 18th annual international conference on Mobile computing and networking
Inference in probabilistic logic programs with continuous random variables
Theory and Practice of Logic Programming
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We consider the problem of localizing a wireless client in an indoor environment based on the signal strength of its transmitted packets as received on stationary sniffers or access points. Several state-of-the-art indoor localization techniques have the drawback that they rely extensively on a labor-intensive 'training' phase that does not scale well. Use of unmodeled hardware with heterogeneous power levels further reduces the accuracy of these techniques. We propose a 'learning-based' approach, WiGEM, where the received signal strength is modeled as a Gaussian Mixture Model (GMM). Expectation Maximization (EM) is used to learn the maximum likelihood estimates of the model parameters. This approach enables us to localize a transmitting device based on the maximum a posteriori estimate. The key insight is to use the physics of wireless propagation, and exploit the signal strength constraints that exist for different transmit power levels. The learning approach not only avoids the labor-intensive training, but also makes the location estimates considerably robust in the face of heterogeneity and various time varying phenomena. We present evaluations on two different indoor testbeds with multiple WiFi devices. We demonstrate that WiGEM's accuracy is at par with or better than state-of-the-art techniques but without requiring any training.