The typical dynamical sampling problem is finding spatial positions X={xi ∈ Rd: i ∈ I} that allow the reconstruction of an unknown function f ∈ H ⊂ L2(Rd) from samples of the function at spatial positions xi ∈ X and subsequent samples of the functions Anf, n=0, ..., L, where A is an evolution operator and n represents time. For example, f can be the temperature at time n=0, A the heat evolution operator, and Anf the temperature at time n. The problem is then to find spatial sampling positions X ⊂ Rd, and end time L, that allow the determination of the initial temperature f from samples {f|X, (Af)|X, ..., (ALf)|X}. Using the relation between an operator and its adjoint we can transform the problem into investigating systems of vectors of form {Anhi: i ∈ I, 0 ≤ n ≤ Li } where {hi}i ∈ I is a countable (finite or infinite) system of vectors in a separable Hilbert space H and A ∈ B(H) is a bounded operator. The problem of dynamical sampling has potential applications in plenacoustic sampling, on-chip sensing, data center temperature sensing, neuron-imaging, and satellite remote sensing, and more generally to Wireless Sensor Networks (WSN). In wireless sensor networks, measurement devices are distributed to gather information about a physical quantity to be monitored, such as temperature, pressure, or pollution. It also has connections and applications to other areas of mathematics including, Banach algebras, C*-algebras, spectral theory of normal operators, and frame theory. In this presentation, we will give an overview of some of the past and recent developments related to the dynamical sampling problem.