Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes. Marin-Perianu, R., Lombriser, C., Havinga, P., Scholten, H., & Tröster, G. Volume 4952. Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes, pages 341-359. Springer Berlin / Heidelberg, 2008.
Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes [link]Website  abstract   bibtex   
Wireless sensor nodes attached to everyday objects and worn by people are able to collaborate and actively assist users in their activities. We propose a method through which wireless sensor nodes organize spontaneously into clusters based on a common context. Provided that the confidence of sharing a common context varies in time, the algorithm takes into account a window-based history of believes. We approximate the behaviour of the algorithm using a Markov chain model and we analyse theoretically the cluster stability. We compare the theoretical approximation with simulations, by making use of experimental results reported from field tests. We show the tradeoff between the time history necessary to achieve a certain stability and the responsiveness of the clustering algorithm.
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 title = {Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes},
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 year = {2008},
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 pages = {341-359},
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 websites = {http://dx.doi.org/10.1007/978-3-540-78731-0_22},
 publisher = {Springer Berlin / Heidelberg},
 city = {Berlin, Heidelberg},
 series = {Lecture Notes in Computer Science},
 chapter = {22},
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 abstract = {Wireless sensor nodes attached to everyday objects and worn by people are able to collaborate and actively assist users in their activities. We propose a method through which wireless sensor nodes organize spontaneously into clusters based on a common context. Provided that the confidence of sharing a common context varies in time, the algorithm takes into account a window-based history of believes. We approximate the behaviour of the algorithm using a Markov chain model and we analyse theoretically the cluster stability. We compare the theoretical approximation with simulations, by making use of experimental results reported from field tests. We show the tradeoff between the time history necessary to achieve a certain stability and the responsiveness of the clustering algorithm.},
 bibtype = {inBook},
 author = {Marin-Perianu, Raluca and Lombriser, Clemens and Havinga, Paul and Scholten, Hans and Tröster, Gerhard},
 book = {The Internet of Things}
}

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