Machine Learning with Limited Signals at Mote-Scale

Опубликовано 3 августа 2018, 14:37

This talk describes the growing role of machine learning in battery-powered wireless sensor networks being deployed in rural and urban areas for environmental and wildlife projection. Specifically, we will discuss radar-based HornNet, deployed in a South African rhino reservation for anti-poaching surveillance, and microphone-based SONYC, deployed in New York City for sound complaint mitigation. Characteristic to these applications are classification and counting tasks with signals that are limited in the sense of being low signal to noise ratio (SNR), with significant spatio-temporal variation across different background clutters, rejection of clutter which may or may be accurate, and potential for interference. We will present how our mote-scale implementations have and are dealing with these challenges.

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