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Open Access Publications from the University of California

Sleeping Coordination for Comprehensive Sensing Using Isotonic Regression and Domatic Partitions

  • Author(s): Koushanfar, Farinaz
  • Taft, Nina
  • Potkonjak, Miodrag
  • et al.
Abstract

We address the problem of energy efficient sensing by adaptively coordinating the sleep schedules of sensor nodes while guaranteeing that values of sleeping nodes can be recovered from the awake nodes within a user’s specified error bound. Our approach has two phases. First, development of models for predicting measurement of one sensor using data from other sensors. Second, creation of the maximal number of subgroups of disjoint nodes, each of whose data is sufficient to recover the measurements of the entire sensor network. For prediction of the sensor measurements, we introduce a new optimal non-parametric polynomial time isotonic regression. Utilizing the prediction models, the sleeping coordination problem is abstracted to a domatic number problem and is optimally solved using an ILP solver. To capture evolving dynamics of the instrumented environment, we monitor the prediction errors occasionally to trigger adaptation of the models and domatic partitions as needed. Experimental evaluations on traces of a medium size network with temperature and humidity sensors indicate that the method can extend the lifetime of the network by a factor of 4 or higher even for a strict error target.

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