PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks

  • Daniela Tulone
  • Samuel Madden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3868)


In this paper, we present a method for approximating the values of sensors in a wireless sensor network based on time series forecasting. More specifically, our approach relies on autoregressive models built at each sensor to predict local readings. Nodes transmit these local models to a sink node, which uses them to predict sensor values without directly communicating with sensors. When needed, nodes send information about outlier readings and model updates to the sink. We show that this approach can dramatically reduce the amount of communication required to monitor the readings of all sensors in a network, and demonstrate that our approach provides provably-correct, user-controllable error bounds on the predicted values of each sensor.


Sensor Network Sensor Node Wireless Sensor Network Local Model Learning Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniela Tulone
    • 1
    • 2
  • Samuel Madden
    • 1
  1. 1.MIT Computer Science and Artificial Intelligence Laboratory 
  2. 2.Computer Science DepartmentUniversity of Pisa 

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