Power-efficient access-point selection for indoor location estimation

By Yiqiang Chen, Qiang Yang, Jie Yin, Xiaoyong Chai. IEEE Transactions on Knowledge and Data Engineering, 2006

298 citations recorded by the source corpus.

How researchers see this paper

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Semi-supervised deep extreme learning machine for Wi-Fi based localization (2015, background): “[19] proposed InfoGain criterion to evaluate the merit of each AP in terms of power and selected the highest ones to form feature.”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “Surprisingly, this problem has received little attention in the existing literature [27].”
Sensor-Based Abnormal Human-Activity Detection (2008, background): “of accuracy, a problem addressed in [ 5 ] for location estimation using access points in a Wireless”
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “For client-based localization systems [173], the computation of location decision is conducted locally on smartphones.”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “RP clustering [84], [106], [108]–[113] Exploitation of APs [89], [101], [112]–[118] Advanced density and weight estimation [84], [119], [120] Sparsity-based localization [109], [111], [116], [121] Assisted localization [92], [107], [122]–[128]”