Pazl: A mobile crowdsensing based indoor WiFi monitoring system

WiFi in indoor environments exhibits spatio-temporal variations in terms of coverage and interference in typical WLAN deployments with multiple APs, motivating the need for automated monitoring to aid network administrators to adapt the WLAN deployment in order to match the user expectations. We develop Pazl, a mobile crowdsensing based indoor WiFi monitoring system that is enabled by a novel hybrid localization mechanism to locate individual measurements taken from participant phones. The localization mechanism in Pazl integrates the best aspects of two well known localization techniques, pedestrian dead reckoning and WiFi fingerprinting; it also relies on crowdsourcing for constructing the WiFi fingerprint database. Compared to existing WiFi monitoring systems based on static sniffers, Pazl is low cost and provides a user-side perspective. Pazl is significantly more automated than wireless site survey tools such as Ekahau Mobile Survey tool by drastically reducing the manual point-and-click based measurement location determination. We implement Pazl through a combination of Android mobile app and cloud backend application on the Google App Engine. Experimental evaluation of Pazl with a trial set of users shows that it yields similar results to manual site surveys but without the tedium.

[1]  Aditya Akella,et al.  Obtaining in-context measurements of cellular network performance , 2012, IMC '12.

[2]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[3]  Philipp Bolliger,et al.  Redpin - adaptive, zero-configuration indoor localization through user collaboration , 2008, MELT '08.

[4]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.

[5]  Ying Zhang,et al.  Localization from mere connectivity , 2003, MobiHoc '03.

[6]  Kang G. Shin,et al.  Sybot: an adaptive and mobile spectrum survey system for wifi networks , 2010, MobiCom.

[7]  Mohamed N. El-Derini,et al.  GAC: Energy-Efficient Hybrid GPS-Accelerometer-Compass GSM Localization , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[8]  Mahadev Satyanarayanan,et al.  Lowering the barriers to large-scale mobile crowdsensing , 2013, HotMobile '13.

[9]  Carsten Isert,et al.  Self-contained indoor positioning on off-the-shelf mobile devices , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[10]  Hojung Cha,et al.  Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem , 2013, Pervasive Mob. Comput..

[11]  Chrisil Arackaparambil,et al.  From MAP to DIST: The Evolution of a Large-Scale WLAN Monitoring System , 2014, IEEE Transactions on Mobile Computing.

[12]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[13]  Seth J. Teller,et al.  Growing an organic indoor location system , 2010, MobiSys '10.

[14]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[15]  Lothar Thiele,et al.  Participatory Air Pollution Monitoring Using Smartphones , 2012 .

[16]  Jim Geier.  Designing and Deploying 802.11n Wireless Networks , 2010 .

[17]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[18]  Srihari Nelakuditi,et al.  SpinLoc: spin once to know your location , 2012, HotMobile '12.

[19]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[20]  Pei Zhang,et al.  Polaris: getting accurate indoor orientations for mobile devices using ubiquitous visual patterns on ceilings , 2012, HotMobile '12.

[21]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[22]  Neil D. Lawrence,et al.  WiFi-SLAM Using Gaussian Process Latent Variable Models , 2007, IJCAI.

[23]  Seth J. Teller,et al.  Implications of device diversity for organic localization , 2011, 2011 Proceedings IEEE INFOCOM.

[24]  Jian Lu,et al.  FTrack: Infrastructure-free floor localization via mobile phone sensing , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[25]  George C. Polyzos,et al.  Report-based topology discovery schemes for centrally-managed Wi-Fi deployments , 2010, 6th EURO-NGI Conference on Next Generation Internet.

[26]  Guoliang Xing,et al.  WizNet: A ZigBee-based sensor system for distributed wireless LAN performance monitoring , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[27]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[28]  Voon Chin Phua,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1999 .

[29]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[30]  Mani Srivastava,et al.  Human-centric sensing , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[31]  Eiman Kanjo,et al.  NoiseSPY: A Real-Time Mobile Phone Platform for Urban Noise Monitoring and Mapping , 2010, Mob. Networks Appl..