A generic objective for a sensor network application is the gathering of data from a field of sensors. Because energy is often scarce in sensor networks, many techniques have been proposed to reduce data size within the network. These techniques either ignore the accuracy of the resulting data, or more often, provide no means for applications to control the resulting accuracy. However in many cases, applications have a quantitative requirement for sensor data accuracy, and the underlying system should meet that efficiently. In this paper, we describe a distributed algorithm that approximates and gathers data in an energy-efficient manner and strictly satisfies an application-provided accuracy requirement. This approximation is based on a hybrid data representation based on linear regression. A distinguishing feature of the proposed algorithm is that it absolutely does not require any models on statistical properties of data and noise, and needs only few general assumptions on sensor node topology. This feature enables the algorithm to serve as a general-purpose mechanism that can be widely used in many scenarios for data gathering-type applications. Simulation experiments with data traces from real environmental data show that it leverages the accuracy requirement to significantly reduce energy consumption.
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