ACM Transaction on Embedded Computing System 1 AIDA: Adaptive Application Independent Data Aggregation in Wireless Sensor Networks1

Sensor networks, a novel paradigm in distributed wireless communication technology, have been proposed for use in various applications including military surveillance and environmental monitoring. These systems could 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 cou-pled 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 adap-tation 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 ag-gregate 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 con-sumption with an overall negative header overhead. Theoretical analysis, simulation, and a test-bed imple-mentation on Berkeley’s MICA motes are provided to validate our claims. 1