TY - JOUR
T1 - Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems
AU - Le, Long N.
AU - Jones, Douglas L.
N1 - Funding Information:
This work was supported in part by the TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation Program sponsored by MARCO and DARPA, and in part by a research grant for the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore's Agency for Science, Technology and Research.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2017/9
Y1 - 2017/9
N2 - 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.
AB - 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.
KW - Guided-processing
KW - IoT
KW - duty-cycling
KW - energy-efficient systems
KW - resource-aware optimization
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U2 - 10.1109/TCSI.2017.2690909
DO - 10.1109/TCSI.2017.2690909
M3 - Article
AN - SCOPUS:85018860117
VL - 64
SP - 2414
EP - 2426
JO - IEEE Transactions on Circuits and Systems I: Regular Papers
JF - IEEE Transactions on Circuits and Systems I: Regular Papers
SN - 1549-8328
IS - 9
M1 - 7924310
ER -