The Tenet architecture for tiered sensor networks

  • Authors:

  • Omprakash Gnawali;Ki-Young Jang;Jeongyeup Paek;Marcos Vieira;Ramesh Govindan;Ben Greenstein;August Joki;Deborah Estrin;Eddie Kohler

  • Affiliations:

  • University of Southern California;University of Southern California;University of Southern California;University of Southern California;University of Southern California;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles

  • Venue:
  • Proceedings of the 4th international conference on Embedded networked sensor systems
  • Year:
  • 2006

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Abstract

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.