Sensor fusion of physical and social data using Web SocialSense on smartphone mobile browsers

Modern smartphones offer a rich selection of onboard sensors, where sensor access is typically performed through API calls provided by the phone's operating system. In this paper we evaluate the viability of implementing sensor processing entirely in the Web browser layer with Web SocialSense, a JavaScript framework for Tizen smartphones that uses a graph topology-based paradigm. This framework enables programmers to write personalized, context-aware applications that can dynamically fuse time-series signals from physical sensors (such as the accelerometer and geolocation services) and social software sensors (such as social network services and personal information management applications). To demonstrate the framework we implemented components for physical sensing and social software sensing to drive two context-aware applications, ActVertisements and Social Map.

[1]  Yuanqing Xia,et al.  Multi-Sensor Data Fusion , 2014 .

[2]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[3]  Ramachandran Ramjee,et al.  PRISM: platform for remote sensing using smartphones , 2010, MobiSys '10.

[4]  Zhigang Liu,et al.  The Jigsaw continuous sensing engine for mobile phone applications , 2010, SenSys '10.

[5]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[6]  Douglas C. Schmidt,et al.  WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones , 2011, Mob. Networks Appl..

[7]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[8]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[9]  Boris Smus Web Audio API , 2013 .

[10]  Archan Misra,et al.  MediAlly: A provenance-aware remote health monitoring middleware , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[11]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[12]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[13]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[14]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[15]  Paul Lukowicz,et al.  Rapid Prototyping of Activity Recognition Applications , 2008, IEEE Pervasive Computing.

[16]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[17]  A. Kansal,et al.  Energy-Accuracy Aware Localization for Mobile Devices , 2009 .

[18]  Deborah Estrin,et al.  AndWellness: an open mobile system for activity and experience sampling , 2010, Wireless Health.

[19]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[20]  Yuan Yu,et al.  Dryad: distributed data-parallel programs from sequential building blocks , 2007, EuroSys '07.