Abstract: This talk introduces a method to design a distributed sensor network for field reconstruction that is minimal with respect to a communication cost function. This cost function is given by the sum of communication between sensors and that of a subset of sensors used for backbone communication.
To achieve this goal, we want to create an observable distributed sensor network, where through the (at most the number of sensors) measurements collected by the central authority, the central authority can recover the initial parameters at different sensors location. To achieve this goal, we need to first decide which sensors should communicate and after design the weights by which each sensor should update their states with those of its neighbors, in other words, the distributed sensor network dynamics. In addition, we need to identify a subset of sensors that can report their state to a central location, corresponding to the design of the backbone
reporting function. The joint design of the sensor network dynamics and the backbone reporting function to recover the initial state of the dynamic system justifies the notion of an observable distributed sensor network.
We show an efficient algorithm for designing the optimal observable distributed sensor network
for a given set of sensors and cost function, providing an illustrative example.