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Sensor selection cost optimisation for tracking structurally cyclic systems: a P-order solution

Doostmohammadian, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1080/00207721.2017.1322640
  3. Abstract:
  4. Measurements and sensing implementations impose certain cost in sensor networks. The sensor selection cost optimisation is the problem of minimising 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 (realisable) states. The idea is to assign sensors to measure states such that the global cost is minimised. 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 minimise 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). © 2017 Informa UK Limited, trading as Taylor & Francis Group
  5. Keywords:
  6. Convex programming ; Linear systems ; State-space models ; Convex optimization ; Costs ; Dynamical systems ; Graph theory ; Large scale systems ; Linear systems ; Optimization ; Polynomial approximation ; Polynomials ; Problem solving ; Sensor networks ; State estimation ; State space methods ; Bounded estimation error ; Graph theoretic approach ; Polynomial expression ; Polynomial-time algorithms ; Sensor measurements ; Sensor selection ; State - space models ; State measurements ; Observability
  7. Source: International Journal of Systems Science ; Volume 48, Issue 11 , 2017 , Pages 2440-2450 ; 00207721 (ISSN)
  8. URL: https://www.tandfonline.com/doi/full/10.1080/00207721.2017.1322640