Abstract
In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.
Highlights
In recent times, mobile crowdsensing (MCS) has obtained a huge attention due to the pervasiveness of smart mobile devices, their in-built sensing abilities, and the fact that they have become an everyday carry item by humans
It consists of GPS stay points, indoor points of interest (POI) within a GPS stay point, neighborhood activity happen during a GPS stay point time duration, but doesn’t capture due to low GPS accuracy in indoor/high rise urban environments, and micro mobility between two GPS stay points
We present the details of the WiFi fingerprint clustering and the similarity metrics used in indoor POI extraction process
Summary
Mobile crowdsensing (MCS) has obtained a huge attention due to the pervasiveness of smart mobile devices, their in-built sensing abilities, and the fact that they have become an everyday carry item by humans. By combining or fusing GPS and WiFi information we intend to identify indoor POI (as first introduced in our previous work [15], and improved POI extraction technique in this paper), and introducing neighborhood activities, and micro mobility analysis information in this paper, by utilizing crowdsensing smartphone data. Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS pattern of a typical user in indoor environments like shopping mall or apartment complex with POI, where users frequently visit, yet it is challenging to identify such POI by only using GPS location data, due to the lack of accuracy in indoor environments. The three main objectives of this article are to understand the distinct POI in indoor environments visited by users, neighborhood activity analysis, and micro mobility analysis.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
