In this talk, I will present a methodology -- Active Wireless Sensing (AWS) -- for rapid and energy-efficient information retrieval in wireless sensor networks. AWS is motivated by emerging technological advances in frequency-agile wideband RF front-ends and inspired by mathematical connections with wideband space-time communication over multipath wireless channels. The basic architecture in AWS consists of: i) a wireless information retriever (WIR) that interrogates a select ensemble of sensors with wideband space-time waveforms, ii) the sensors acting as active scatterers to generate a multipath ensemble response to the interrogation signal, and iii) the WIR retrieving the sensor data by exploiting the location-dependent space-time signatures of individual sensors. A key mechanism for energy efficiency is distributed source-channel matching: generating a "coherent" space-time response from sub-ensembles of sensors with highly correlated data, either due to signal field correlation or the spatial scale of sensor cooperation. Source-channel matching at varying spatial scales leads to a fundamental tradeoff between spatio-temporal multiplexing and the received SNR that governs the capacity and reliability of information retrieval. I will conclude the talk with a discussion of the challenges and opportunities in AWS due to the presence of multipath scattering between the WIR and the sensor ensemble.
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