In-network data estimation for sensor-driven scientific applications

  • Authors:

  • Nanyan Jiang;Manish Parashar

  • Affiliations:

  • Center for Autonomic Computing, The Applied Software Systems Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ;Center for Autonomic Computing, The Applied Software Systems Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ

  • Venue:
  • HiPC'08 Proceedings of the 15th international conference on High performance computing
  • Year:
  • 2008

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Abstract

Sensor networks employed by scientific applications oftenneed to support localized collaboration of sensor nodes to perform in-network data processing. This includes new quantitative synthesis andhypothesis testing in near real time, as data streaming from distributedinstruments, to transform raw data into high level domain-dependent information. This paper investigates in-network data processing mechanismswith dynamic data requirements in resource constrained heterogeneoussensor networks. Particularly, we explore how the temporaland spatial correlation of sensor measurements can be used to trade offbetween the complexity of coordination among sensor clusters and thesavings that result from having fewer sensors involved in in-network processing,while maintaining an acceptable error threshold. Experimentalresults show that the proposed in-network mechanisms can facilitate theefficient usage of resources and satisfy data requirement in the presenceof dynamics and uncertainty.