Title :
Energy-efficient self-adapting online linear forecasting for wireless sensor network applications
Author :
Lim, Jai-Jin ; Shin, Kang G.
Author_Institution :
Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI
Abstract :
New energy-efficient linear forecasting methods are proposed for various sensor network applications, including in-network data aggregation and mining. The proposed methods are designed to minimize the number of trend changes for a given application-specified forecast quality metric. They also self-adjust the model parameters, the slope and the intercept, based on the forecast errors observed via measurements. As a result, they incur O(1) space and time overheads, a critical advantage for resource-limited wireless sensors. An extensive simulation study based on real-world and synthetic time-series data shows that the proposed methods reduce the number of trend changes by 20%~50% over the existing well-known methods for a given forecast quality metric. That is, they are more predictive than the others with the same forecast quality metric
Keywords :
data mining; time series; wireless sensor networks; data mining; energy-efficient self-adapting forecasting; in-network data aggregation; online linear forecasting; time-series data; wireless sensor network applications; Application software; Data mining; Design methodology; Energy efficiency; Energy measurement; Load forecasting; Monitoring; Prediction algorithms; Predictive models; Wireless sensor networks;
Conference_Titel :
Mobile Adhoc and Sensor Systems Conference, 2005. IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-9465-8
DOI :
10.1109/MAHSS.2005.1542822
