NDLI: A CSMA-based approach for detecting composite data aggregate events with collaborative sensors in WSN

Content Provider	IEEE Xplore Digital Library
Author	Chi Yang Kaijun Ren Zhimin Yang Peng Gong Chang Liu
Copyright Year	2011
Description	Author affiliation: College of Computer, National University of Defense Technology, Changsha, Hunan, 410073, China (Kaijun Ren) \|\| Department of Computer, Shandong University at Weihai, Weihai, China (Zhimin Yang) \|\| School of Computer Science and Software Engineering, The University of Western Australia, M002, 35 Stirling Highway, Crawley, WA6009, Australia (Chi Yang) \|\| Anhui Wenda Information and Technology College, Hefei, 231201, China (Peng Gong) \|\| CS3-Centre for Complex Software Systems and Services, Swinburne University of Technology, Melbourne, 3122, Australia (Chang Liu)
Abstract	Nowadays, wireless sensor networks are widely used to monitor real time events and answer the ad hoc queries from a certain member node. However, computing and maintaining the information of aggregate queries in event monitoring wireless sensor networks incurs high spatial and temporal overhead for storage and transmission where potentially high volumes of unnecessary data may run through with changing time. Failure of processing that data can lead to unsuccessful event detection which can be very dangerous and costly in real world application. In order to reduce the overhead caused by unnecessary data for aggregate, suppression techniques such as data fusion, data sharing, data prediction, lossless data compression and base station side query rewriting are widely discussed in the WSN research community. In this paper, a technique which makes use of the spatial data relationship of local sensor nodes collaboratively is proposed to rein the detection of composite events with data aggregate. An empirical study is carried out to show the efficiency of the new technique. In addition, the new algorithm is compared to the previous event detection algorithms without spatial data suppression technique to demonstrate the significant performance gains.
Starting Page	489
Ending Page	496
File Size	376060
Page Count	8
File Format	PDF
ISBN	9781457703867
e-ISBN	9781457703874
DOI	10.1109/CSCWD.2011.5960117
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-08
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Aggregates Event detection Peer to peer computing Sensor phenomena and characterization Monitoring Partial Aggregate WSN Data Aggregate Composite Event Data Suppression
Content Type	Text
Resource Type	Article

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