AIDA: Adaptive application-independent data aggregation in wireless sensor networks

  • Authors:

  • Tian He;Brian M. Blum;John A. Stankovic;Tarek Abdelzaher

  • Affiliations:

  • Department of Computer Science, University of Virginia, Charlottesville, VA;Department of Computer Science, University of Virginia, Charlottesville, VA;Department of Computer Science, University of Virginia, Charlottesville, VA;Department of Computer Science, University of Virginia, Charlottesville, VA

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS)
  • Year:
  • 2004

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Abstract

Sensor networks, a novel paradigm in distributed wireless communication technology, have been proposed for various applications including military surveillance and environmental monitoring. These systems deploy heterogeneous collections of sensors capable of observing and reporting on various dynamic properties of their surroundings in a time sensitive manner. Such systems suffer bandwidth, energy, and throughput constraints that limit the quantity of information transferred from end-to-end. These factors coupled with unpredictable traffic patterns and dynamic network topologies make the task of designing optimal protocols for such networks difficult. Mechanisms to perform data-centric aggregation utilizing application-specific knowledge provide a means to augmenting throughput, but have limitations due to their lack of adaptation and reliance on application-specific decisions. We, therefore, propose a novel aggregation scheme that adaptively performs application-independent data aggregation in a time sensitive manner. Our work isolates aggregation decisions into a module that resides between the network and the data-link layer and does not require any modifications to the currently existing MAC and network layer protocols. We take advantage of queuing delay and the broadcast nature of wireless communication to concatenate network units into an aggregate using a novel adaptive feedback scheme to schedule the delivery of this aggregate to the MAC layer for transmission. In our evaluation we show that end-to-end transmission delay is reduced by as much as 80% under heavy traffic loads. Additionally, we show as much as a 50% reduction in transmission energy consumption with an overall reduction in header overhead. Theoretical analysis, simulation, and a test-bed implementation on Berkeley's MICA motes are provided to validate our claims.