Deployment of a vast array of tiny smart sensors (sensor devices with processors) interconnected over wireless channels are enabling their pervasive use in a variety of defense and commercial applications, such as environmental monitoring (e.g. traffic, habitat, security), industrial sensing and diagnostics (e.g. factory, appliances), infrastructures (e.g. power grid, water distributions, waste disposal), and battlefield awareness (e.g. multi-target tracking). While the task of developing and implementing pervasive applications for such a scenario is exciting, it poses tremendous challenges. In this context, design of algorithms for processing information in sensor networks is an emerging research area. However, due to the unique characteristics of the sensor network computing paradigm, the objective is to design algorithms that meet the two contradicting goals of low power and fast executions times, i.e., the algorithms should be energy efficient and they should exploit the parallel computing resources available in sensor nodes. In this paper, we present a power-time efficient algorithm for computing Fast Fourier Transform over data distributed across smart sensors. The Fast Fourier Transform (FFT) [2] has been studied extensively as a frequency analysis tool in diverse application areas such as audio, signal, and image processing, and several other real time data applications [5].
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