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The Internet is evolving from a network of comput- ers to a network of devices, e.g., phones, smart meters, traffic cameras, and air quality sensors. In this Internet of Things, large amounts of data generated by everyday objects can often be organized into data streams, where each data stream is a time series of sensor values sampled together. Visualization is an easy-to-use, efficient, and effective method to present this heterogeneous data to large and diverse audiences, and enable its analysis by users without programming background. Although general data-storage and sharing systems for the Internet of Things, like Pachube and Sensor.Network, offer some basic visualizations, they do little to help users understand relations and patterns hidden in the data, nor do they support live updates to the underlying data streams. Other systems, like Biketastic and the Copenhagen Wheel, feature more complex visualizations but are tailored for a specific application domain and do not address heterogeneous data streams. In this paper, we present SNViz, a Web browser-based AJAX application built using Protovis for visual analysis of large, heterogeneous, and live data streams in the Internet of Things. Besides offering panning and zooming for a detailed look at smaller data subsets, SNViz offers brush-and- linking across multiple visualizations. The latter is invaluable in helping users understand and analyze relationships and patterns hidden in the data. Although SNViz currently works by accessing JSON representations of data streams from Sensor.Network over HTTP, it can be extended to work with other data sources (e.g., wireless sensor network devices or smartphones) and even customized for specific applications.
This work was presented at the Internet of Things conference (www.iot2010.org).