A magic lens for revealing device interactions in smart environments

Keeping track of device interactions in smart environments is a challenging task for everyday users. Given the expected high number of communicating devices in future smart homes, it will become increasingly important to put users more in control of their smart environments by providing tools to monitor and control the interactions between smart objects and remote services. We present a system for collecting and visualizing interactions ofWeb-enabled smart things andWeb services in an intuitive and visually appealing way. Our tool displays device interactions both using a Web-based visualization application and in the form of a "magic lens" by augmenting the camera view of a tablet with relevant connections between recognized devices in the camera's field of view.

[1]  Stefan Saroiu,et al.  Home automation in the wild: challenges and opportunities , 2011, CHI.

[2]  George Danezis,et al.  Privacy-preserving smart metering , 2011, ISSE.

[3]  Simon Mayer,et al.  Searching in a web-based infrastructure for smart things , 2012, 2012 3rd IEEE International Conference on the Internet of Things.

[4]  José L. Martínez Lastra,et al.  Semantic web services in factory automation: fundamental insights and research roadmap , 2006, IEEE Transactions on Industrial Informatics.

[5]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[6]  Simon Mayer,et al.  Moving Application Logic from the Firmware to the Cloud: Towards the Thin Server Architecture for the Internet of Things , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[7]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[8]  Blaine A. Price,et al.  A Principled Taxonomy of Software Visualization , 1993, J. Vis. Lang. Comput..

[9]  W. Keith Edwards,et al.  More than meets the eye: transforming the user experience of home network management , 2008, DIS '08.

[10]  Nicholas J. P. Race,et al.  CANVIS: context-aware network visualization using smartphones , 2005, Mobile HCI.

[11]  D. Randall Living Inside a Smart Home: A Case Study , 2003 .

[12]  Silvia Santini,et al.  Automatic socio-economic classification of households using electricity consumption data , 2013, e-Energy '13.

[13]  Rik Van de Walle,et al.  Configuration of smart environments made simple: Combining visual modeling with semantic metadata and reasoning , 2014, 2014 International Conference on the Internet of Things (IOT).

[14]  Stefanie Zollmann,et al.  Smart Vidente: advances in mobile augmented reality for interactive visualization of underground infrastructure , 2013, Personal and Ubiquitous Computing.

[15]  Friedemann Mattern,et al.  Virtual Time and Global States of Distributed Systems , 2002 .

[16]  Lauri Malmi,et al.  A comprehensive taxonomy of algorithm animation languages , 2010, J. Vis. Lang. Comput..

[17]  Simon Mayer,et al.  Demo: uncovering device whispers in smart homes , 2012, MUM.

[18]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[19]  Norbert A. Streitz,et al.  User requirements for intelligent home environments: a scenario-driven approach and empirical cross-cultural study , 2005, sOc-EUSAI '05.

[20]  Mark W. Newman,et al.  The Work to Make a Home Network Work , 2005, ECSCW.

[21]  Pavel Minarík,et al.  NetFlow Data Visualization Based on Graphs , 2008, VizSEC.

[22]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[23]  Gabriela Csurka,et al.  Incorporating Geometry Information with Weak Classifiers for Improved Generic Visual Categorization , 2005, ICIAP.

[24]  Huirong Fu,et al.  Privacy Issues of Vehicular Ad-Hoc Networks , 2010 .

[25]  Wilhelm Kleiminger,et al.  Opportunistic Sensing for Efficient Energy Usage in Private Households , 2011 .