Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Energy efficiency is highly desirable for sensing systems in the Internet of Things. 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 paper shows that such an approach is not necessarily optimal in energy efficiency, and proposes 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 with the duty-cycling approach.