A factor graph approach to link loss monitoring in wireless sensor networks

The highly stochastic nature of wireless environments makes it desirable to monitor link loss rates in wireless sensor networks. In a wireless sensor network, link loss monitoring is particularly supported by the data aggregation communication paradigm of network traffic: the data collecting node ca...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2005-04, Vol.23 (4), p.820-829
Main Authors: Yongyi Mao, Kschischang, F.R., Baochun Li, Pasupathy, S.
Format: Article
Language:English
Subjects:
Algorithms
Bandwidth
Complexity
Computer networks
Condition monitoring
Factor graphs
Fault tolerance
Graphs
Inference algorithms
Intelligent networks
Interference constraints
Iterative algorithms
link loss monitoring
Links
Mathematical models
Monitoring
network tomography
Networks
Satellite communications
Sensor fusion
sensor networks
Sensors
sum-product algorithm
Wireless networks
Wireless sensor networks
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Summary:The highly stochastic nature of wireless environments makes it desirable to monitor link loss rates in wireless sensor networks. In a wireless sensor network, link loss monitoring is particularly supported by the data aggregation communication paradigm of network traffic: the data collecting node can infer link loss rates on all links in the network by exploiting whether packets from various sensors are received, and there is no need to actively inject probing packets for inference purposes. In this paper, we present a low complexity algorithmic framework for link loss monitoring based on the recent modeling and computational methodology of factor graphs. The proposed algorithm iteratively updates the estimates of link losses upon receiving (or detecting the loss of) recently sent packets by the sensors. The algorithm exhibits good performance and scalability, and can be easily adapted to different statistical models of networking scenarios. In particular, due to its low complexity, the algorithm is particularly suitable as a long-term monitoring facility.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2005.843557