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Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks

Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks. Maurice Chu, Horst Haussecker and Feng Zhao Xerox Palo Alto Research Center International Journal of High Performance Computing Applications, 2002. Motivation.

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Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks

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  1. Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks Maurice Chu, Horst Haussecker and Feng Zhao Xerox Palo Alto Research Center International Journal of High Performance Computing Applications, 2002

  2. Motivation Advances in wireless networking, microfabrication (MEMS) and embedded microprocessors have enabled a new generation of large sensor networks.

  3. Problem How to dynamically query sensors and route data in a network so that information gain is maximized while latency and bandwidth consumption is minimized.

  4. Paper proposal Information-driven sensor querying (IDSQ) to optimize sensor selection. Constrained anisotropic diffusion routing (CADR) to direct data routing and incrementally combine sensor measurements so as to minimize an overall cost function.

  5. What is new? The use of a general form of information utility that models the information content as well as the spatial configuration of a network in a distributed way.

  6. Main Idea “Allows sensors to become activated when there are interesting events to report and only those parts of the network with the most useful information balanced by the communication cost need to be active”

  7. Formal Formulation Sensing Model and Measure of Uncertainty General model: Zi = time dependent sensor characteristics λi(t) = sensors characteristics X(t) = parameters

  8. Application of the formal formulation to acoustic sensors Stationary problem: sensors localization and sensors characteristics. Assumptions: measure only the amplitude. Signals propagate Isotropically.

  9. Belief Representation of the current a posteriori distribution of x, given measurements Z1, … Zn. Estimation: minimum mean square estimation. The approximation of the residual uncertainty is determined by the covariance

  10. Criteria to select the next best sensor In general, belief is applied to centralized approaches. To apply it, in the sensor networks, we have to consider communication. We cannot flood the network.

  11. Sensor Selection Task: select an optimal subset of sensors and to decide to an optimal order of how to incorporate these measures in the belief update. Representation: the uncertainty is represented as a Gaussian (ellipsoid) distribution in a state space. Error: difference between distribution Gaussian area and the area after a sensor is added.

  12. Utility function All distribution probability in a space R. Assign to each value of R its uncertainty. Calculate a new distribution probability after adding a new sensor. Compare sensors to find the best utility value.

  13. Information Utility Measure Information content is inversely related to the “size” of the higher probability uncertainty region of the estimate of x.

  14. Techniques Covariance: square approximation of the ellipsoid. Fisher Information matrix is based on the class of likelihood densities in a space: surface area of the high probability region. Entropy of Estimation Uncertainty: entropy is the log of the volume of the set of typical values for a random variable. It should be used in the case that the distribution is not Gaussian.

  15. Techniques Volume of High Probability Region: related to the probability of values distributions. Sensor Geometry Based Measures: function of the geometric location of the sensor.

  16. Composite Objective Function Node is a leader. The leader holds the current belief. Information is sent to the leader from nodes of a certain distance. Objective function: a function of both information utility and cost of bandwidth and latency: utility function minus the cost (bandwidth plus latency) between the sensor j and sensor i.

  17. Updating the Belief State Assuming that that x is uniformly distributed in a subset of R and that z is conditionally independent of x, the probability of every values z of x is determined by:

  18. Information-Driven Sensor Query Assumptions: all sensors are synchronized; All node labeled (1 to n); clustering algorithm; Algorithm for choosing a leader. Communication between the leader and nodes in the cluster (leader knows characteristics of the nodes). Calculate the belief space. Start to track sensors that have being incorporated into the belief space. Evaluate Belief Quality test Incorporate new nodes until quality is good.

  19. Constrained Anisotropic Diffusion Route Choose the sensor that satisfies the constraints and maximizes the objective function in the neighborhood (in communication distance). Choose the next routing node in the direction of the gradient of the objective function. Propagate the belief evaluation and the necessary information to recalculate it.

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