Carlos Rautenberg

Carlos Rautenberg erforscht die optimale Platzierung von Sensornetzwerken zur Steigerung der Energieeffizienz. Seine Arbeit adressiert das praktische Problem, wie Messstationen in verteilten Systemen so angeordnet werden können, dass sie mit minimalem Energieeinsatz maximale Informationen liefern — relevant für Smart-Grid-Anwendungen, Gebäudeautomation, Umweltmonitoring und industrielle Prozessüberwachung. Durch mathematische Optimierungsverfahren entwickelt er Methoden, um Sensornetzwerke kostengünstiger und nachhaltiger zu gestalten.

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• Numerik und Optimierung von robusten Gleichgewichtsproblemen

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• Optimal Network Sensor Placement for Energy Efficiency

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• The Infinite-Dimensional Optimal Filtering Problem with Mobile and Stationary Sensor Networks

  2014

  Numerical Functional Analysis and Optimization · DOI

  In this article, we introduce a framework to address filtering and smoothing with mobile sensor networks for distributed parameter systems. The main problem is formulated as the minimization of a functional involving the trace of the solution of a Riccati integral equation with constraints given by the trajectory of the sensor network. We prove existence and develop approximation of the solution to the Riccati equation in certain trace-class spaces. We also consider the corresponding optimization problem. Finally, we employ a Galerkin approximation scheme and implement a descent algorithm to compute optimal trajectories of the sensor network. Numerical examples are given for both stationary and moving sensor networks.

• A distributed parameter control approach to optimal filtering and smoothing with mobile sensor networks

  2009

  DOI

  In this paper we present a framework to address filtering and smoothing problems for distributed parameter systems when mobile (dynamic) sensors are used to provide system measurements. This framework can be used for systems governed by parabolic and hyperbolic partial differential equations and hence has application to a diverse set of problems such as estimating locations of biological and chemical sources, target tracking and estimation. We formulate the problems as hybrid systems on infinite dimensional spaces (coupled systems of partial, ordinary and delay differential equations) and use infinite dimensional theory to develop computational algorithms for the problems. A simple numerical example illustrates the approach.