@INCOLLECTION{boracchiICANN11,
  author = {Alippi, Cesare and Boracchi, Giacomo and Roveri, Manuel},
  title = {A Distributed Self-adaptive Nonparametric Change-Detection Test for
	Sensor/Actuator Networks},
  booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2011},
  publisher = {Springer Berlin / Heidelberg},
  year = {2011},
  editor = {Honkela, Timo and Duch, Wlodzislaw and Girolami, Mark and Kaski,
	Samuel},
  volume = {6792},
  series = {Lecture Notes in Computer Science},
  pages = {173-180},
  note = {10.1007/978-3-642-21738-8_23},
  abstract = {The prompt detection of faults and, more in general, changes in stationarity
	in networked systems such as sensor/actuator networks is a key issue
	to guarantee robustness and adaptability in applications working
	in real-life environments. Traditional change-detection methods aiming
	at assessing the stationarity of a data generating process would
	require a centralized availability of all observations, solution
	clearly unacceptable when large scale networks are considered and
	data have local interest. Differently, distributed solutions based
	on decentralized change-detection tests exploiting information at
	the unit and cluster level would be a solution. This work suggests
	a novel distributed change-detection test which operates at two-levels:
	the first, running on the unit, is particularly reactive in detecting
	small changes in the process generating the data, whereas the second
	exploits distributed information at the cluster-level to reduce false
	positives. Results can be immediately integrated in the machine learning
	community where adaptive solutions are envisaged to address changes
	in stationarity of the considered application. A large experimental
	campaign shows the effectiveness of the approach both on synthetic
	and real data applications.},
  affiliation = {Dipartimento di Elettronica e Informazione, Politecnico di Milano,
	Milano, Italy},
  isbn = {978-3-642-21737-1},
  keyword = {Computer Science},
  url = {http://dx.doi.org/10.1007/978-3-642-21738-8_23}
}