Power optimization in disk-based real-time application specific systems
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Scheduling techniques for reducing processor energy use in MacOS
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Efficient indexing for broadcast based wireless systems
Mobile Networks and Applications - Special issue on mobile computing and system services
Power management techniques for mobile communication
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Machine Learning
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
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
On Localized Prediction for Power Efficient Object Tracking in Sensor Networks
ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
DRPM: dynamic speed control for power management in server class disks
Proceedings of the 30th annual international symposium on Computer architecture
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
High-level goal recognition in a wireless LAN
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Fuzzy location and tracking on wireless networks
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
Kernel-Based Positioning in Wireless Local Area Networks
IEEE Transactions on Mobile Computing
A dynamic system approach for radio location fingerprinting in wireless local area networks
IEEE Transactions on Communications
Movement detection for power-efficient smartphone WLAN localization
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Learning to Infer the Status of Heavy-Duty Sensors for Energy-Efficient Context-Sensing
ACM Transactions on Intelligent Systems and Technology (TIST)
Kernel-based particle filtering for indoor tracking in WLANs
Journal of Network and Computer Applications
Uncaught signal imputation for accuracy enhancement of WLAN-based positioning systems
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
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An important goal of indoor location estimation systems is to increase the estimation accuracy while reducing the power consumption. In this paper, we present a novel algorithm known as CaDet for power-efficient location estimation by intelligently selecting the number of Access Points (APs) used for location estimation. We show that by employing machine learning techniques, CaDet is able to use a small subset of the APs in the environment to detect a client's location with high accuracy. CaDet uses a combination of information theory, clustering analysis, and a decision tree algorithm. By collecting data and testing our algorithms in a realistic WLAN environment in the computer science department area of the Hong Kong University of Science and Technology, we show that CaDet (Clustering and Decision Tree-based method) can be much higher in accuracy as compared to other methods. We also show through experiments that, by intelligently selecting APs, we are able to save the power on the client device while achieving the same level of accuracy.