Abstract
Indoor localization has been recognized as a promising research around the world, and fingerprint-based localization method which leverages WIFI Received Signal Strength (RSS) has been extensively studied since widespread deployment of Access Points (APs) makes WIFI signals omnipresent and easily be obtained. A primary weakness of WIFI-based fingerprinting localization approach lies in its vulnerability under environmental changes and alteration of AP deployment. Despite some studies focus on dealing with effects of AP alterations and low-dynamic environmental factors, such as humidity, temperature, etc., influences of high-dynamic factors, such as changes of crowds' density and position, on WIFI radio map have not been sufficiently studied. In this work, we propose OWUH, an Online Learning-based WIFI Radio Map Updating service considering influences of high-dynamic factors. OWUH utilizes sensors in smart phones as the source of RSS datasets, and it combines historical and newly collected RSS data and purposeful probe data as dataset to incrementally update radio map, which means, compared with traditional methods, the OWUH approach requires a smaller number of RSS data for frequent updating of radio map. Moreover, in order to further enlarge our dataset, we take static data and low-dynamic data into account. An improved online learning method is proposed to recognize periodic pattern and update current radio map. Extensive experiments with 15 volunteers across 10 days indicate that OWUH effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with mean errors of less than 5dB, outperforming existing approaches.
Highlights
Indoor localization has always been an urgently needed service in our society, which can be used for indoor navigation, daily activities tracking and many other amazing applications [1], [2]
Low-dynamic data enlarge our original dataset and further improve the accuracy of radio map updating, which has been proved by our experience
WIFI-based localization is vulnerable to environmental factors and, in this work, we mainly aim to address the effects of high-dynamic factors such as crowdsâ density and location on localization performance
Summary
Indoor localization has always been an urgently needed service in our society, which can be used for indoor navigation, daily activities tracking and many other amazing applications [1], [2]. A variety of wireless indoor localization techniques based on signals like WIFI, RFID, sound, etc., have thrived [3]â[6]. Considering omnipresent WIFI signals and a lot of WIFI infrastructures (Access Point, AP) in a building, WIFI fingerprint-based indoor localization. The main drawback of WIFI signals is the stability. Many environmental factors such as temperature, humidity, wavelength, moving objects, dynamic movement of human, etc., as well as source-related factors such as power or APs location alteration, can affect RSS enormously, which result in unexpected deviation between RSS samples of online location query and that of offline
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