NDLI: Content Based Pre-diagnosis for Wireless Sensor Networks

Content Provider	IEEE Xplore Digital Library
Author	Jiangwu Nie Huadong Ma
Copyright Year	2011
Abstract	The network diagnosis is significant and difficult because the limited resource in WSNs, especially the heavy communication burden for diagnosis. How to reduce the diagnosis overhead is crucial. In this paper, we argue that the sensing data from sensor networks presents another new model called DSD for network diagnosis. We discover that the character of the sensing data reflect the network status in some way, according to considerable experiments in the Green Orbs project. We mine the hints between the sensing data and the failure in the sensor networks, and maintain a failure knowledge library which can update through self-learning. Through this diagnosis mechanism, we deduce the node failures and the network failures without any additional network burden, and the final diagnosis report can give the network administrators good reference for further diagnosis. In this way, we make full use of the sensing data to do the pre-diagnosis according to the characteristics of the sensing data. This approach avoids sending metrics periodically from sensor nodes. Moreover, the failure library can improve the efficiency of diagnosis. We analysis the three months sensing data from the Green Orbs project, analytically and experimentally, we show that the proposed scheme can improve the diagnosis performance with low energy cost.
Starting Page	98
Ending Page	104
File Size	618006
Page Count	7
File Format	PDF
ISBN	9781457721786
DOI	10.1109/MSN.2011.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Wireless sensor networks Green products Debugging wireless sensor network diagnosis Libraries Sensors Batteries sensing data
Content Type	Text
Resource Type	Article

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