Multi-Sensor Data-Driven Synchronization Using Wearable Sensors
Terrell R. Bennett, Nicholas Gans, Roozbeh Jafari

Citation
Terrell R. Bennett, Nicholas Gans, Roozbeh Jafari. "Multi-Sensor Data-Driven Synchronization Using Wearable Sensors". ISWC 2015, ACM (ed.), 113-116, 9, September, 2015.

Abstract
In this paper, we present a method to synchronize the data streams from multiple sensors, including wearables and sensors in the environment. Our approach exploits common events observed by the sensors as they interact. This allows for synchronization of low-power embedded systems in heterogeneous sensor networks by using the events in the physical world to drive the synchronization in the cyber world. Using the events, a sensor-accuracy delay model, and a graph model, we detect physical and cyber couplings between the sensor data streams and determine which couplings will minimize the overall clock drift and time keeping discrepancies. We present a graph model to represent the event couplings between sensors and the drift in the sensor timing. We propose a solution that employs a shortest path algorithm, selects the best set of physical and cyber couplings to update the local time of the sensors, and minimizes the overall clock drift in the system based on the graph model. Our results for two experiments show an improvement of 21.5% and 43.7% for total drift and 59.4% and 60.7% for average drift.

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Citation formats  
  • HTML
    Terrell R. Bennett, Nicholas Gans, Roozbeh Jafari. <a
    href="http://www.terraswarm.org/pubs/539.html"
    >Multi-Sensor Data-Driven Synchronization Using Wearable
    Sensors</a>, ISWC 2015, ACM (ed.), 113-116, 9,
    September, 2015.
  • Plain text
    Terrell R. Bennett, Nicholas Gans, Roozbeh Jafari.
    "Multi-Sensor Data-Driven Synchronization Using
    Wearable Sensors". ISWC 2015, ACM (ed.), 113-116, 9,
    September, 2015.
  • BibTeX
    @inproceedings{BennettGansJafari15_MultiSensorDataDrivenSynchronizationUsingWearableSensors,
        author = {Terrell R. Bennett and Nicholas Gans and Roozbeh
                  Jafari},
        title = {Multi-Sensor Data-Driven Synchronization Using
                  Wearable Sensors},
        booktitle = {ISWC 2015},
        editor = {ACM},
        pages = {113-116},
        day = {9},
        month = {September},
        year = {2015},
        abstract = {In this paper, we present a method to synchronize
                  the data streams from multiple sensors, including
                  wearables and sensors in the environment. Our
                  approach exploits common events observed by the
                  sensors as they interact. This allows for
                  synchronization of low-power embedded systems in
                  heterogeneous sensor networks by using the events
                  in the physical world to drive the synchronization
                  in the cyber world. Using the events, a
                  sensor-accuracy delay model, and a graph model, we
                  detect physical and cyber couplings between the
                  sensor data streams and determine which couplings
                  will minimize the overall clock drift and time
                  keeping discrepancies. We present a graph model to
                  represent the event couplings between sensors and
                  the drift in the sensor timing. We propose a
                  solution that employs a shortest path algorithm,
                  selects the best set of physical and cyber
                  couplings to update the local time of the sensors,
                  and minimizes the overall clock drift in the
                  system based on the graph model. Our results for
                  two experiments show an improvement of 21.5% and
                  43.7% for total drift and 59.4% and 60.7% for
                  average drift.},
        URL = {http://terraswarm.org/pubs/539.html}
    }
    

Posted by Barb Hoversten on 21 Apr 2015.
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