TY - GEN
T1 - AdaSens
T2 - 27th Asia and South Pacific Design Automation Conference, ASP-DAC 2022
AU - Lan, Shuyue
AU - Wang, Zhilu
AU - Mamish, John
AU - Hester, Josiah
AU - Zhu, Qi
N1 - Funding Information:
Acknowledgements: We gratefully acknowledge the support from National Science Foundation awards CNS-2038853, CNS-1850496, IIS-1724341, CNS-1834701, and Office of Naval Research grant N00014-19-1-2496.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Perceiving the environment for better and more efficient situational awareness is essential in applications such as wildlife surveillance, wildfire detection, crop irrigation, and building management. Energy-harvesting, intermittently-powered sensors have emerged as a zero maintenance solution for long-term environmental perception. However, these devices suffer from intermittent and varying energy supply, which presents three major challenges for executing perceptual tasks: (1) intelligently scaling computation in light of constrained resources and dynamic energy availability, (2) planning communication and sensing tasks, (3) and coordinating sensor nodes to increase the total perceptual range of the network. We propose an adaptive framework, AdaSens, which adapts the operations of intermittently-powered sensor nodes in a coordinated manner to cover as much as possible of the targeted scene, both spatially and temporally, under interruptions and constrained resources. We evaluate AdaSens on a real-world surveillance video dataset, VideoWeb, and show at least 16% improvement on the coverage of the important frames compared with other methods.
AB - Perceiving the environment for better and more efficient situational awareness is essential in applications such as wildlife surveillance, wildfire detection, crop irrigation, and building management. Energy-harvesting, intermittently-powered sensors have emerged as a zero maintenance solution for long-term environmental perception. However, these devices suffer from intermittent and varying energy supply, which presents three major challenges for executing perceptual tasks: (1) intelligently scaling computation in light of constrained resources and dynamic energy availability, (2) planning communication and sensing tasks, (3) and coordinating sensor nodes to increase the total perceptual range of the network. We propose an adaptive framework, AdaSens, which adapts the operations of intermittently-powered sensor nodes in a coordinated manner to cover as much as possible of the targeted scene, both spatially and temporally, under interruptions and constrained resources. We evaluate AdaSens on a real-world surveillance video dataset, VideoWeb, and show at least 16% improvement on the coverage of the important frames compared with other methods.
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U2 - 10.1109/ASP-DAC52403.2022.9712501
DO - 10.1109/ASP-DAC52403.2022.9712501
M3 - Conference contribution
AN - SCOPUS:85126124870
T3 - Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
SP - 556
EP - 561
BT - ASP-DAC 2022 - 27th Asia and South Pacific Design Automation Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 January 2022 through 20 January 2022
ER -