Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems
For IoT system designers, this paper demonstrates that guided-processing can significantly outperform the widely used duty-cycling approach, offering a new paradigm for energy-efficient sensing.
This work proposes guided-processing as a fundamentally better alternative to duty-cycling for energy-efficient IoT sensing systems, achieving up to 1.7× reduction in false-alarm rate and 4× reduction in miss rate for the same energy consumption in an audio sensing application.
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.