  Energy-efficiency is highly desirable for sensing systems in the Internet of
Things (IoT). A common approach to achieve low-power systems is duty-cycling,
where components in a system are turned off periodically to meet an energy
budget. However, this work shows that such an approach is not necessarily
optimal in energy-efficiency, and proposes \textit{guided-processing} as a
fundamentally better alternative. The proposed approach offers 1) explicit
modeling of performance uncertainties in system internals, 2) a realistic
resource consumption model, and 3) a key insight into the superiority of
guided-processing over duty-cycling. Generalization from the cascade structure
to the more general graph-based one is also presented. Once applied to optimize
a large-scale audio sensing system with a practical detection application,
empirical results show that the proposed approach significantly improves the
detection performance (up to $1.7\times$ and $4\times$ reduction in false-alarm
and miss rate, respectively) for the same energy consumption, when compared to
the duty-cycling approach.
