Asynchronous in-network prediction: efficient aggregation in sensor networks

Abstract

Given a sensor network and aggregate queries over the values sensed by subsets of nodes in the network, how do we ensure that high quality results are served for the maximum possible time&quest. The issues underlying this question relate to the fidelity of query results and lifetime of the network. To maximize both, we propose a novel technique called asynchronous in-network prediction incorporating two computationally efficient methods for in-network prediction of partial aggregate values. These values are propagated via a tree whose construction is cognizant of (a) the coherency requirements associated with the queries, (b) the remaining energy at the sensors, and (c) the communication and message processing delays. Finally, we exploit in-network filtering and in-network aggregation to reduce the energy consumption of the nodes in the network. Experimental results over real world data support our claim that, for aggregate queries with associated coherency requirements, a prediction-based, asynchronous scheme provides higher quality results for a longer amount of time than a synchronous scheme. Also, whereas aggregate dissemination techniques proposed so far for sensor networks appear to have to trade-off quality of data for energy efficiency, we demonstrate that this is not always necessary.