capacity and energy aware activation of sensor nodes for area phenomenon using wireless network transport
We consider a sensor network employing sensor nodes that have been placed in specific locations. An area phenomenon is detected and tracked by the activated sensors. The area phenomenon is modelled to consist of K spatially distributed point phenomena. The activated sensors collect data samples characterizing the parameters of the involved component point phenomena. They compress the observed data readings and transport them to a processing center. The center processes the received data to derive estimates of the component point phenomena's parameters. Our sensing stochastic process models account for distance dependent observation noise perturbations as well as for location dependent observation noise correlations. At the processing center, sample mean calculations are used to derive estimates of the underlying area phenomenon's parameters. We develop a computationally efficient algorithm for determining the specific set of sensors selected for activation under capacity and energy resource constraints, so that a sufficiently low reproduction distortion level is attained. We demonstrate our algorithm to yield distortion levels that are quite close to those characterized by a lower bound function.
sensor selection heuristic algorithm distortion lower bound rate allocation wireless sensor network