Collaborative Sensing for Heterogeneous Sensor Networks

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Collaborative Sensing for Heterogeneous Sensor Networks
	Xiao, Kejiang 1; Wang, Rui 2; Cai, Cai 3; Zeng, Shaohua 1; Mahmood, Zahid 4
刊名	JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
	2016-07-01
卷号	32 期号:4 页码:947-968
关键词	Bayesian filtering dynamic environments collaboration sampling frequency target classification wireless sensor networks
ISSN号	1016-2364
英文摘要	Collaboration between low-quality sensor and high-quality sensor can achieve trade-off between accuracy and energy efficiency in heterogeneous sensor networks (HSNs). Generally, HSNs are deeply integrated with dynamic physical environments. The monitored target's dynamics are the most important and common factors of the dynamic environments. Some important parameters (such as active opportunity, sampling frequency and sampling time) fail to adapt to the changes, which undermines the collaboration's performance when the state of the monitored target changes. Even the system performance is not up to user requirements or large amounts of energy are consumed. To solve this problem, we propose an adaptive collaboration scheme named EasiAC by the collaboration between magnetic and camera sensors. First, for the dynamics of the monitored target, EasiAC utilizes the magnetic sensors to predict the target's state via Bayesian filtering based method. Second, to achieve good performance of such collaboration, EasiAC adjusts the camera sensors' active opportunity, optimal sampling frequency and sampling time dynamically according to the estimated results from the magnetic sensors. Finally, a boosting based algorithm named BbTC is proposed to make classification for the target to achieve high accuracy (average classification accuracy is more than 98%). We evaluate EasiAC method through extensive simulations and real road environment experiments. The results demonstrate that EasiAC needs less energy consumption (saving 97% energy) than traditional solutions, while maintaining the performance at acceptable level (average image integration ratio is 90%) in the presence of target's dynamics.
资助项目	National Natural Science Foundation of China (NSFC)[61379134]
WOS研究方向	Computer Science
语种	英语
出版者	INST INFORMATION SCIENCE
WOS记录号	WOS:000378461400008
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/8355]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Rui
作者单位	1.Informat & Commun Co State Grid Hunan Elect Power, Changsha 410007, Hunan, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Changsha Environm Protect Coll, Dept Basic Curriculums, Changsha 410004, Hunan, Peoples R China 4.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Xiao, Kejiang,Wang, Rui,Cai, Cai,et al. Collaborative Sensing for Heterogeneous Sensor Networks[J]. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,2016,32(4):947-968.
APA	Xiao, Kejiang,Wang, Rui,Cai, Cai,Zeng, Shaohua,&Mahmood, Zahid.(2016).Collaborative Sensing for Heterogeneous Sensor Networks.JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,32(4),947-968.
MLA	Xiao, Kejiang,et al."Collaborative Sensing for Heterogeneous Sensor Networks".JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 32.4(2016):947-968.