Sensor Selection Cost Optimization for Tracking Structurally Cyclic Systems: a P-Order Solution
For control and estimation of large-scale cyber-physical systems, this work provides a computationally efficient solution to a previously combinatorial problem, though limited to structurally cyclic systems.
The paper addresses sensor selection cost optimization for tracking structurally cyclic systems, proposing a polynomial-time graph-theoretic approach that solves the problem as a linear sum assignment with O(m³) complexity, minimizing sensing cost while ensuring observability.
Measurements and sensing implementations impose certain cost in sensor networks. The sensor selection cost optimization is the problem of minimizing the sensing cost of monitoring a physical (or cyber- physical) system. Consider a given set of sensors tracking states of a dynamical system for estimation purposes. For each sensor assume different costs to measure different (realizable) states. The idea is to assign sensors to measure states such that the global cost is minimized. The number and selection of sensor measurements need to ensure the observability to track the dynamic state of the system with bounded estimation error. The main question we address is how to select the state measurements to minimize the cost while satisfying the observability conditions. Relaxing the observability condition for structurally cyclic systems, the main contribution is to propose a graph theoretic approach to solve the problem in polynomial time. Note that, polynomial time algorithms are suitable for large-scale systems as their running time is upper-bounded by a polynomial expression in the size of input for the algorithm. We frame the problem as a linear sum assignment with solution complexity of O(m3).