Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors

doi:10.1145/3448416

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors
	Liang, Yunji 1; Wang, Xin 2; Yu, Zhiwen 1; Guo, Bin 1; Zheng, Xiaolong 3; Samtani, Sagar 4
刊名	ACM TRANSACTIONS ON SENSOR NETWORKS
	2021-08-01
卷号	17 期号:3 页码:28
关键词	Energy efficiency latent correlation learning collaboration sensing internet of things temporal convolutional network attention mechanism multi-task learning
ISSN号	1550-4859
DOI	10.1145/3448416
通讯作者	Liang, Yunji(liangyunji@nwpu.edu.cn)
英文摘要	With the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energyefficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors. Finally, to decrease the sampling frequency of energy-intensive sensors, we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors. To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks.
资助项目	National Major Program for Technological Innovation[2018AAA0100500] ; natural science foundation of China[61902320] ; fundamental research funds for the central universities[31020180QD140]
WOS关键词	CONTINUOUS AUTHENTICATION ; INTELLIGENCE ; INDUSTRY ; INTERNET ; DEVICES
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	ASSOC COMPUTING MACHINERY
WOS记录号	WOS:000777801400013
资助机构	National Major Program for Technological Innovation ; natural science foundation of China ; fundamental research funds for the central universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48325]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Liang, Yunji
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China 2.Northwestern Polytech Univ, Sch Software, Xian 710129, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Indiana Univ, Kelley Sch Business, Bloomington, IN 47405 USA
推荐引用方式 GB/T 7714	Liang, Yunji,Wang, Xin,Yu, Zhiwen,et al. Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors[J]. ACM TRANSACTIONS ON SENSOR NETWORKS,2021,17(3):28.
APA	Liang, Yunji,Wang, Xin,Yu, Zhiwen,Guo, Bin,Zheng, Xiaolong,&Samtani, Sagar.(2021).Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors.ACM TRANSACTIONS ON SENSOR NETWORKS,17(3),28.
MLA	Liang, Yunji,et al."Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors".ACM TRANSACTIONS ON SENSOR NETWORKS 17.3(2021):28.