Name
Title | ||
|---|---|---|
A distributed self-adaptive nonparametric change-detection test for sensor/actuator networks |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21738-8_23 | ICANN (2) |
Keywords | Field | DocType |
adaptive solution,real data application,centralized availability,change-detection test,self-adaptive nonparametric change-detection test,actuator network,large scale network,decentralized change-detection test,large experimental campaign,considered application,traditional change-detection method,fault detection | Change detection,Computer science,Real-time computing,Robustness (computer science),Artificial intelligence,Distributed computing,Adaptability,Fault detection and isolation,Exploit,Nonparametric statistics,Machine learning,False positive paradox,Actuator | Conference |
Volume | ISSN | Citations |
6792 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cesare Alippi | 1 | 1040 | 115.84 |
| Giacomo Boracchi | 2 | 324 | 30.49 |
| Manuel Roveri | 3 | 272 | 30.19 |