Managing Energy and Data Quality in Large Sensor Swarms
Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing

Citation
Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. " Managing Energy and Data Quality in Large Sensor Swarms". Talk or presentation, 29, September, 2013; Poster from the First International Workshop on the Swarm at the Edge of the Cloud (SEC'13 @ ESWeek), Montreal.

Abstract
Modern applications typically require measuring several variables for extended periods of time over a large area. To meet the application requirements, the design and deployment has to carefully balance two competing goals: (1) high spatio-temporal resolution to ensure the accuracy of the collected data, and (2) minimal energy consumption to maximize the network lifetime and limit node maintenance. The amount of data that each node collects and processes directly affects both its power consumption and the accuracy of the information obtained. Extending the uptime of a swarm node - especially when they are deployed in difficult to access locations – is an active topic of research. Common approaches try to enhance the battery life directly by harvesting energy from the environment and employing low-power hardware, or using improved wireless protocols and distributed computation for data processing. More recently, researchers are optimizing the battery life indirectly by reducing the overall amount of sensed data. Here, the data is selectively sampled according to a predetermined protocol, reducing the total amount of samples collected by the individual sensor nodes, thus minimizing the energy consumption. To maintain an acceptable amount of total measurements, the missing data is inferred according to statistical models that capture how the data evolves. In addition to enhancing the battery life, these approaches are also able to estimate any lost or corrupted data, making them a popular choice. We recently proposed an energy efficient, data driven technique to estimate missing data within a heterogeneous sensor network [1]. The latent variable factorization model which typically considers only dyadic interactions in data has been extended to multivariate spatio-temporal data, by applying tensor decomposition techniques. The key advantage of using a latent variable model is that it provides a compact representation of the gathered data that can be used to recover the missing samples. In order to perform well under extreme sampling conditions, we explicitly incorporate the spatial, temporal, and inter-sensor correlations. The study focuses on the trade-off between the accuracy in recovering the missing data and the energy consumption when sensor nodes duty cycle to save energy. The proposed technique drastically reduces the amount of sampled data at each node, thus allowing the nodes to spend more time in a low-power sleep mode and save energy. The lower amount of sampled data implies a lower amount of data to transmit from the node to a central gathering station, reducing also the power consumptions associated with the radio communications. Our experiments with the OMNeT++ network simulator using realistic wireless channel conditions, on data collected from two real-world sensor networks, show that we can sample just 20% of the data and can reconstruct the remaining 80% of the data with less than 9% mean error, outperforming similar state-of-the-art techniques such is distributed compressive sampling. In addition, energy savings ranging up to 76%, depending on the sampling rate and the hardware configuration of the node. The next step in our studies is to develop a technique to decide how often latent variables need to be recomputed online to adapt to ever-changing environmental conditions, and to then apply our strategy to a large, swarm size deployment, that covers most of San Diego County. The data from the deployment has 1000s of sensors over an area size of 100x100 sq miles, and has been collected over the period of last 10 years.

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Citation formats  
  • HTML 
    Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. <a
href="http://www.terraswarm.org/pubs/132.html"><i>
Managing Energy and Data Quality in Large Sensor
Swarms</i></a>, Talk or presentation,  29,
September, 2013; Poster from the <a
href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
>First International Workshop on the Swarm at the Edge of
the Cloud (SEC'13 @ ESWeek)</a>, Montreal.
  • Plain text 
    Jinseok Yang, Sameer Tilak, Tajana Simunic Rosing. "
Managing Energy and Data Quality in Large Sensor
Swarms". Talk or presentation,  29, September, 2013;
Poster from the <a
href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
>First International Workshop on the Swarm at the Edge of
the Cloud (SEC'13 @ ESWeek)</a>, Montreal.
  • BibTeX 
    @presentation{YangTilakRosing13_ManagingEnergyDataQualityInLargeSensorSwarms,
    author = {Jinseok Yang and Sameer Tilak and Tajana Simunic
              Rosing},
    title = { Managing Energy and Data Quality in Large Sensor
              Swarms},
    day = {29},
    month = {September},
    year = {2013},
    note = {Poster from the <a
              href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
              >First International Workshop on the Swarm at the
              Edge of the Cloud (SEC'13 @ ESWeek)</a>, Montreal.},
    abstract = {Modern applications typically require measuring
              several variables for extended periods of time
              over a large area. To meet the application
              requirements, the design and deployment has to
              carefully balance two competing goals: (1) high
              spatio-temporal resolution to ensure the accuracy
              of the collected data, and (2) minimal energy
              consumption to maximize the network lifetime and
              limit node maintenance. The amount of data that
              each node collects and processes directly affects
              both its power consumption and the accuracy of the
              information obtained. Extending the uptime of a
              swarm node - especially when they are deployed in
              difficult to access locations – is an active
              topic of research. Common approaches try to
              enhance the battery life directly by harvesting
              energy from the environment and employing
              low-power hardware, or using improved wireless
              protocols and distributed computation for data
              processing. More recently, researchers are
              optimizing the battery life indirectly by reducing
              the overall amount of sensed data. Here, the data
              is selectively sampled according to a
              predetermined protocol, reducing the total amount
              of samples collected by the individual sensor
              nodes, thus minimizing the energy consumption. To
              maintain an acceptable amount of total
              measurements, the missing data is inferred
              according to statistical models that capture how
              the data evolves. In addition to enhancing the
              battery life, these approaches are also able to
              estimate any lost or corrupted data, making them a
              popular choice. We recently proposed an energy
              efficient, data driven technique to estimate
              missing data within a heterogeneous sensor network
              [1]. The latent variable factorization model which
              typically considers only dyadic interactions in
              data has been extended to multivariate
              spatio-temporal data, by applying tensor
              decomposition techniques. The key advantage of
              using a latent variable model is that it provides
              a compact representation of the gathered data that
              can be used to recover the missing samples. In
              order to perform well under extreme sampling
              conditions, we explicitly incorporate the spatial,
              temporal, and inter-sensor correlations. The study
              focuses on the trade-off between the accuracy in
              recovering the missing data and the energy
              consumption when sensor nodes duty cycle to save
              energy. The proposed technique drastically reduces
              the amount of sampled data at each node, thus
              allowing the nodes to spend more time in a
              low-power sleep mode and save energy. The lower
              amount of sampled data implies a lower amount of
              data to transmit from the node to a central
              gathering station, reducing also the power
              consumptions associated with the radio
              communications. Our experiments with the OMNeT++
              network simulator using realistic wireless channel
              conditions, on data collected from two real-world
              sensor networks, show that we can sample just 20%
              of the data and can reconstruct the remaining 80%
              of the data with less than 9% mean error,
              outperforming similar state-of-the-art techniques
              such is distributed compressive sampling. In
              addition, energy savings ranging up to 76%,
              depending on the sampling rate and the hardware
              configuration of the node. The next step in our
              studies is to develop a technique to decide how
              often latent variables need to be recomputed
              online to adapt to ever-changing environmental
              conditions, and to then apply our strategy to a
              large, swarm size deployment, that covers most of
              San Diego County. The data from the deployment has
              1000s of sensors over an area size of 100x100 sq
              miles, and has been collected over the period of
              last 10 years.},
    URL = {http://terraswarm.org/pubs/132.html}
}

Posted by Christopher Brooks on 1 Oct 2013.

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