Citation
Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. "Leveraging application context for efficient sensing". IEEE ISSNIP 2014, 21, April, 2014.

Abstract
Today's platforms for long-term environmental monitoring (e.g. buoys or towers) typically host large solar panels and batteries. Ideally, miniaturized platforms could be used instead, so state of the art power management technique that takes into account battery levels and harvested energy to provide uniform sampling rate. However, the fixed pre-defined intervals is not desirable. The state-of-art adaptive sampling mechanism, optimal adaptive sampling algorithm (OSA) uses data uncertainty and past measurements to determine the optimal sampling rate at the cost of high computational complexity O(n3), thus draining the batteries even further. Even if the sampling were done optimally, there are still significant challenges with data transmission. The state of the art approach for determining optimal transmission policy offers limited control over the energy-delay tradeoff and is not suitable to support wide range of applications ranging from real-time and delay-tolerant. To address these challenges, we have developed a novel power management framework that adapts sampling and transmission rates based on battery level, energy harvesting level and application-context (e.g. characteristics of the gathered data). Our framework is optimal in terms of energy efficiency with low computational complexity. We evaluate the performance of the proposed framework using datasets from two real-world deployments. Our results show that our approach saves significant amounts of energy (between 20% to 60%) by avoiding oversampling when the application does not need it and uses this saved energy to support sampling at high rates to capture event with necessary fidelity when needed.

Electronic downloads

• https://www.src.org/library/publication/p069326/. (Link to publication on SRC website.)
• http://dx.doi.org/10.1109/ISSNIP.2014.6827692

• HTML

  Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. <a
  href="http://www.terraswarm.org/pubs/251.html"
  >Leveraging application context for efficient
  sensing</a>, IEEE ISSNIP 2014, 21, April, 2014.

• Plain text

  Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing.
  "Leveraging application context for efficient
  sensing". IEEE ISSNIP 2014, 21, April, 2014.

• BibTeX

  @inproceedings{YangTilakRosing14_LeveragingApplicationContextForEfficientSensing,
      author = {Jinseok Yang and Sameer Tilak and Tajana Simunic
                Rosing},
      title = {Leveraging application context for efficient
                sensing},
      booktitle = {IEEE ISSNIP 2014},
      day = {21},
      month = {April},
      year = {2014},
      abstract = {Today's platforms for long-term environmental
                monitoring (e.g. buoys or towers) typically host
                large solar panels and batteries. Ideally,
                miniaturized platforms could be used instead, so
                state of the art power management technique that
                takes into account battery levels and harvested
                energy to provide uniform sampling rate. However,
                the fixed pre-defined intervals is not desirable.
                The state-of-art adaptive sampling mechanism,
                optimal adaptive sampling algorithm (OSA) uses
                data uncertainty and past measurements to
                determine the optimal sampling rate at the cost of
                high computational complexity O(n3), thus draining
                the batteries even further. Even if the sampling
                were done optimally, there are still significant
                challenges with data transmission. The state of
                the art approach for determining optimal
                transmission policy offers limited control over
                the energy-delay tradeoff and is not suitable to
                support wide range of applications ranging from
                real-time and delay-tolerant. To address these
                challenges, we have developed a novel power
                management framework that adapts sampling and
                transmission rates based on battery level, energy
                harvesting level and application-context (e.g.
                characteristics of the gathered data). Our
                framework is optimal in terms of energy efficiency
                with low computational complexity. We evaluate the
                performance of the proposed framework using
                datasets from two real-world deployments. Our
                results show that our approach saves significant
                amounts of energy (between 20% to 60%) by avoiding
                oversampling when the application does not need it
                and uses this saved energy to support sampling at
                high rates to capture event with necessary
                fidelity when needed.},
      URL = {http://terraswarm.org/pubs/251.html}
  }
  

Posted by Jinseok Yang on 4 Feb 2014.
Groups: services