Name
Title | ||
|---|---|---|
On-line reconstruction of missing data in sensor/actuator networks by exploiting temporal and spatial redundancy |
Abstract | ||
|---|---|---|
Data streams from remote monitoring systems such as wireless sensor networks show immediately that the “you sample you get” statement is not always true. Not rarely, the data stream is interrupted by intermittent communication or sensors faults, resulting in missing data in the received sequence. This has a negative impact in many algorithms assuming continuous data stream; as such, the missing data must be suitably reconstructed, in order to guarantee continuous data availability. We suggest a general methodology for reconstructing missing data that exploits both temporal and spatial redundancy characterizing the phenomenon being monitored and the distributed system, a situation proper of many monitoring systems constituted by sensor and actuator networks. Temporal and spatial dependencies are learned through linear and non-linear non-parametric models, also encompassing neural -possibly recurrent- networks, which become the spatial transfer functions connecting the different views of the phenomenon under investigation. Missing data are finally reconstructed by exploiting the forecasting ability provided by such transfer functions. The experimental section shows the effectiveness of the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IJCNN.2012.6252689 | Neural Networks |
Keywords | Field | DocType |
actuators,data handling,forecasting theory,recurrent neural nets,redundancy,sensors,actuator networks,continuous data availability,data stream,forecasting ability,intermittent communication,linear nonparametric model,missing data reconstruction,nonlinear nonparametric model,online reconstruction,recurrent neural networks,remote monitoring systems,sensor faults,sensor networks,spatial dependencies,spatial redundancy,temporal dependencies,temporal redundancy,transfer functions,Missing data,distributed monitoring systems,fault accommodation,non-linear reconstruction,recurrent neural networks | Data mining,Data stream mining,Computer science,Data stream,Recurrent neural network,Real-time computing,Redundancy (engineering),Artificial intelligence,Missing data,Transfer function,Wireless sensor network,Group method of data handling,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 E-ISBN : 978-1-4673-1489-3 | 978-1-4673-1489-3 | 6 |
PageRank | References | Authors |
0.52 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cesare Alippi | 1 | 1040 | 115.84 |
| Giacomo Boracchi | 2 | 324 | 30.49 |
| Manuel Roveri | 3 | 272 | 30.19 |