Building efficient wireless sensor networks with low-level naming

  • Authors:

  • John Heidemann;Fabio Silva;Chalermek Intanagonwiwat;Ramesh Govindan;Deborah Estrin;Deepak Ganesan

  • Affiliations:

  • USC/Information Sciences Institute, Marina del Rey, CA;USC/Information Sciences Institute, Marina del Rey, CA;USC/Information Sciences Institute, Marina del Rey, CA;USC/Information Sciences Institute, Marina del Rey, CA;USC/Information Sciences Institute, Marina del Rey, CA and University of California, Los Angeles, Los Angeles, CA;USC/Information Sciences Institute, Marina del Rey, CA and University of California, Los Angeles, Los Angeles, CA

  • Venue:
  • SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
  • Year:
  • 2001

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Abstract

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational testbed.