Citation
David Cohen. "Managing Resources on A Multi-Modal Sensing Device For Energy-Aware State Estimation". Master's thesis, University of Illinois, Urbana-Champaign, August, 2013.

Abstract
Multi-modal sensing devices are becoming more and more prevalent in everyday life. Whether it be in the form of a smartphone, mobile computing device, remote sensor node, or a sensor-packed robot, they are used almost everywhere. Often these devices run on battery power or on energy harvested from the environment. In these situations, energy is at a premium, and resources must be intelligently managed to balance energy consumption and system performance. A methodology is developed for joint sensor scheduling and state estimation on an energy-constrained device. The approach is similar to existing sensor scheduling methods for hidden Markov models. These methods are extended, and the problem is cast as a standard partially observable Markov decision process (POMDP), for which numerous exact and approximate solutions are well known. Then optimal sensing policies are demonstrated on a vehicle detection application. A sensing platform is developed consisting of an ultra-low power MSP430 Micro Controller Unit (MCU), a high-performance ARM-based MCU, a passive infrared motion sensor, and a camera. This platform is capable of 100_ energy scalability between sensing modalities. Appropriate POMDP model parameters are extracted from real data traces, and these are used to evaluate the expected performance of optimal sensing policies across a range of energy levels. These policies are then run on real data in order to compare actual performance to theoretical performance. We show that this performance gap is small in most cases, demonstrating both the theoretical and practical value of our sensor management techniques.

Electronic downloads

• https://www.ideals.illinois.edu/handle/2142/45372

• HTML

  David Cohen. <a
  href="http://www.terraswarm.org/pubs/91.html"
  ><i>Managing Resources on A Multi-Modal Sensing
  Device For Energy-Aware State
  Estimation</i></a>, Master's thesis,  University
  of Illinois, Urbana-Champaign, August, 2013.

• Plain text

  David Cohen. "Managing Resources on A Multi-Modal
  Sensing Device For Energy-Aware State Estimation".
  Master's thesis,  University of Illinois, Urbana-Champaign,
  August, 2013.

• BibTeX

  @mastersthesis{Cohen13_ManagingResourcesOnMultiModalSensingDeviceForEnergyAware,
      author = {David Cohen},
      title = {Managing Resources on A Multi-Modal Sensing Device
                For Energy-Aware State Estimation},
      school = {University of Illinois, Urbana-Champaign},
      month = {August},
      year = {2013},
      abstract = {Multi-modal sensing devices are becoming more and
                more prevalent in everyday life. Whether it be in
                the form of a smartphone, mobile computing device,
                remote sensor node, or a sensor-packed robot, they
                are used almost everywhere. Often these devices
                run on battery power or on energy harvested from
                the environment. In these situations, energy is at
                a premium, and resources must be intelligently
                managed to balance energy consumption and system
                performance. A methodology is developed for joint
                sensor scheduling and state estimation on an
                energy-constrained device. The approach is similar
                to existing sensor scheduling methods for hidden
                Markov models. These methods are extended, and the
                problem is cast as a standard partially observable
                Markov decision process (POMDP), for which
                numerous exact and approximate solutions are well
                known. Then optimal sensing policies are
                demonstrated on a vehicle detection application. A
                sensing platform is developed consisting of an
                ultra-low power MSP430 Micro Controller Unit
                (MCU), a high-performance ARM-based MCU, a passive
                infrared motion sensor, and a camera. This
                platform is capable of 100_ energy scalability
                between sensing modalities. Appropriate POMDP
                model parameters are extracted from real data
                traces, and these are used to evaluate the
                expected performance of optimal sensing policies
                across a range of energy levels. These policies
                are then run on real data in order to compare
                actual performance to theoretical performance. We
                show that this performance gap is small in most
                cases, demonstrating both the theoretical and
                practical value of our sensor management
                techniques. },
      URL = {http://terraswarm.org/pubs/91.html}
  }
  

Posted by Mila MacBain on 13 Aug 2013.