An Interactive Context-aware Power Management Technique for Optimizing Sensor Network Lifetime
Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing

Citation
Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. "An Interactive Context-aware Power Management Technique for Optimizing Sensor Network Lifetime". Unpublished article, 2013.

Abstract
A key problem in sensor networks equipped with renewable energy sources is deciding how do you allocate energy to various tasks (sensing, communication etc.) over time so that the deployed network continues to gather highquality data. The state-of-the-art energy allocation algorithm (Progressive Filling) takes into account current battery level and harvesting energy and fairly allocates as much energy as possible along the time dimension. In this paper we show that by not considering application-context this approach leads to very high and uniform sampling rates. However, sampling the environment at fixed predefined intervals is neither possible (need to accommodate system failures) nor desirable (sampling rate might not capture an important event with desired fidelity). To that end, in this paper we propose a novel interactive power management technique that adapts sampling rate as a function of both application-level context (e.g., user request) and system-level context (e.g harvesting energy availability). We vary several key parameters including application request patterns, geographic locations, time slot length, battery end point voltage and evaluate the performance of our approach in terms of energy efficiency and accuracy. Our simulations use sensor data and system specifications (battery and solar panel specs, sensing and communication costs) from a real sensor network deployment. Our results show that the proposed approach saves significant amounts of energy by avoiding oversampling when application does not need it and uses this saved energy to support sampling at high rates to capture event with necessary fidelity when needed. The computational complexity of our approach is lower ( O(n)) than the state-of-the-art non-interactive energy allocation algorithm (O(n2)).

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Citation formats

• HTML

  Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. <a
  href="http://www.terraswarm.org/pubs/98.html"
  ><i>An Interactive Context-aware Power Management
  Technique for Optimizing Sensor Network
  Lifetime</i></a>, Unpublished article,  2013.

• Plain text

  Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. "An
  Interactive Context-aware Power Management Technique for
  Optimizing Sensor Network Lifetime". Unpublished
  article,  2013.

• BibTeX

  @unpublished{YangTilakRosing13_InteractiveContextawarePowerManagementTechniqueForOptimizing,
      author = {Jinseok Yang and Sameer Tilak and Tajana Simunic
                Rosing},
      title = {An Interactive Context-aware Power Management
                Technique for Optimizing Sensor Network Lifetime},
      year = {2013},
      abstract = {A key problem in sensor networks equipped with
                renewable energy sources is deciding how do you
                allocate energy to various tasks (sensing,
                communication etc.) over time so that the deployed
                network continues to gather highquality data. The
                state-of-the-art energy allocation algorithm
                (Progressive Filling) takes into account current
                battery level and harvesting energy and fairly
                allocates as much energy as possible along the
                time dimension. In this paper we show that by not
                considering application-context this approach
                leads to very high and uniform sampling rates.
                However, sampling the environment at fixed
                predefined intervals is neither possible (need to
                accommodate system failures) nor desirable
                (sampling rate might not capture an important
                event with desired fidelity). To that end, in this
                paper we propose a novel interactive power
                management technique that adapts sampling rate as
                a function of both application-level context
                (e.g., user request) and system-level context (e.g
                harvesting energy availability). We vary several
                key parameters including application request
                patterns, geographic locations, time slot length,
                battery end point voltage and evaluate the
                performance of our approach in terms of energy
                efficiency and accuracy. Our simulations use
                sensor data and system specifications (battery and
                solar panel specs, sensing and communication
                costs) from a real sensor network deployment. Our
                results show that the proposed approach saves
                significant amounts of energy by avoiding
                oversampling when application does not need it and
                uses this saved energy to support sampling at high
                rates to capture event with necessary fidelity
                when needed. The computational complexity of our
                approach is lower ( O(n)) than the
                state-of-the-art non-interactive energy allocation
                algorithm (O(n2)).},
      URL = {http://terraswarm.org/pubs/98.html}
  }
  

Posted by Mila MacBain on 21 Aug 2013.