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CSN offers a unique vantage point as well with respect to the physical phenomena in which the system is embedded. Given a valid forward solution for the phenomenon of interest (e.g., the heat equation), it may be possible to formulate questions about the structure of the sensor network as inverse problems.
We start with a simple problem and cover it in detail to illustrate the ideas behind the CNS approach. We first propose a computational model for temperature variation during a 24-hour period. This model is then incorporated into a one-SEL S-Net in order to report any period during which the sampled temperature values are invalid with respect to (1) the temperature model, or (2) the sensor model....
We have proposed Computational Sensor Networks as a methodology1 to exploit models of physical phenomena in order to better understand the structure of the sensor network. To do so, it is necessary to relate changes in the sensed variables (e.g., temperature) to the aspect of interest in the sensor network (e.g., sensor node position, sensor bias, etc.), and to develop a computational method for its...
Computational Sensor Networks1 depend on phenomenological models which describe spatio-temporal relations between physical quantities. This generally requires a common coördinate frame of reference. Almost all calculation depends on functions defined with respect to x, y, z, and t (e.g., the heat equation relates the partial derivative of temperature with respect to time to the second derivative of...
In this chapter, we describe an algorithm to solve the S-cluster leadership problem [55]. For a good introduction to distributed algorithms, including solutions to variations of the leadership problem and correctness proofs, see [104]. For a leadership election protocol in the context of target tracking, see [168]. The algorithm presented here is optimal with respect to the number of broadcasts, and...
Biological systems exhibit an amazing array of distributed sensor/actuator systems, and the exploitation of principles and practices found in nature will lead to more effective artificial systems. The retina is an example of a highly tuned sensing organ, and the human skin is comprised of a set of heterogeneous sensor and actuator elements. Moreover, the specific organization and architecture of these...
This chapter introduces a Bayesian approach for the estimation of distributed phenomena based on discrete time-space measurements obtained by a sensor network. We introduce a new methodology for sensor network applications, which rigorously exploits mathematical models of the distributed phenomenon to be monitored. By this unobtrusive exploitation, the individual sensor nodes collect information not...
Computational Sensor Networks1 represent a scientific computing approach, and this includes the Verification and Validation (V & V) methodology of that discipline [118]; that is, model implementations must be verified (e.g., for numerical properties like error and convergence), and appropriate tests embedded in the system to monitor system correctness during execution. However, an important new...
As stated in the introduction to the book, the CSN approach is based on the analysis of models of the sensor network, the physical phenomena, and the application scenario1. We apply this here to show that the exploitation of nonmobile, distributed sensor and communication devices by a team of mobile robots offers performance advantages in terms of speed, energy, robustness and communication requirements...
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