Paper Info
Title
Light-weight network health monitoring
Abstract
As the application of WSNs for long-term monitoring purposes becomes real, the issue of WSN system health monitoring grows increasingly important. Manually understanding the root causes of an observed behavior is time-consuming and difficult, often knowledge of prior behavior is necessary for understanding the potential risk on the long-term system performance. The challenges lie in the balance between the amount of system data collected and the level of detail in which state can be inferred from this data. In this paper, we propose a lightweight runtime logging and corresponding network state inference mechanism that enables scalable WSN health monitoring. Concretely, we propose that nodes only report their internal state on the occurrence of important events. Having a very low computational complexity and message overhead within the sensor network, reported events are analyzed at a less constrained network sink.
Year
DOI
Venue
2012
10.1145/2185677.2185701
IPSN
Keywords
Field
DocType
system data,long-term system performance,important event,long-term monitoring purpose,light-weight network health monitoring,sensor network,internal state,corresponding network state inference,network sink,scalable wsn health monitoring,wsn system health monitoring,level of detail,wireless sensor network,wireless sensor networks,computational complexity,data collection,environmental monitoring,data analysis,system performance,long term
Key distribution in wireless sensor networks,System health monitoring,Computer science,Level of detail,Inference,Computer network,Real-time computing,Wireless sensor network,Environmental monitoring,Computational complexity theory,Scalability
Conference
Citations 
PageRank 
References 
1
0.35
3
Authors
5
Name
Order
Citations
PageRank
Yi-Hsuan Chiang1363.83
Matthias Keller2766.07
Roman Lim320018.35
Polly Huang488682.35
Jan Beutel510.35