TafLoc: Time-adaptive and fine-grained device-free localization with little cost

• Main Authors: CHANG, Liqiong, Jie XIONG, CHEN, Xiaojiang, WANG, Ju, HU, Junhao, WANG, Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Time Adaptive; Fine-grained; Device Free Localization; Received Signal Strength; Computer Sciences; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3391; https://ink.library.smu.edu.sg/context/sis_research/article/4392/viewcontent/TafLoc.pdf
• Institution: Singapore Management University

Many emerging applications drive the needs of device-free localization (DfL), in which the target can be localized without any device attached. Because of the ubiquitousness of WiFi infrastructures nowadays, the widely available Received Signal Strength (RSS) information at the WiFi Access points are commonly employed for localization purposes. However, current RSS based DfL systems have one main drawback hindering their real-life applications. That is, the RSS measurements (fingerprints) vary slowly in time even without any change in the environment and frequent updates of RSS at each location lead to a high human labor cost. In this paper, we propose an RSS based low cost DfL system named TafLoc which is able to accurately localize the target over a long time scale. To reduce the amount of human labor cost in updating the RSS fingerprints, TafLoc represents the RSS fingerprints as a matrix which has several unique properties. Based on these properties, we propose a novel fingerprint matrix reconstruction scheme to update the whole fingerprint database with just a few RSS measurements, thus the labor cost is greatly reduced. Extensive experiments illustrate the effectiveness of TafLoc, outperforming the state-of-the-art RSS based DfL systems.