Name
Abstract | ||
|---|---|---|
We present a statistical method that uses prediction modeling to decrease the temporally redundant data transmitted back to the sink. The major novelties are fourfold: First, a prediction model is fit to the sensor data. Second, prediction error is utilized to adaptively update the model parameters using hypothesis testing. Third, a data transformation is proposed to bring the sensor sample series closer to weak stationarity. Finally, an efficient implementation is presented. We show that our proposed preDiction eRror bASed hypoThesis testInG (DRASTIG) method achieves low energy dissipation while keeping the prediction errors at user-defined tolerable magnitudes based on real data experiments. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1218556.1218560 | TOSN |
Keywords | Field | DocType |
hypothesis testing,prediction model,data transformation,wireless sensor networks,wireless sensor network,prediction error,data model,hypothesis test,regression,prediction,stationary,data acquisition | Data mining,Mean squared prediction error,Low energy,Regression,Computer science,Dissipation,Data acquisition,Wireless sensor network,Sink (computing),Statistical hypothesis testing | Journal |
Volume | Issue | ISSN |
2 | 4 | 1550-4859 |
Citations | PageRank | References |
4 | 0.75 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Arici | 1 | 370 | 17.80 |
| Toygar Akgun | 2 | 90 | 9.39 |
| Yucel Altunbasak | 3 | 1507 | 116.78 |