Wireless Sensor Network (WSN) is fixed in many sensing environments to capture and monitor events. The sensing values come from sensor device that may contain noise, missing values, and redundant features. Noise, missing values and redundant features should be removed from the streamed data using an efficient preprocessing mechanism and then preprocessed data can be provided for further processing such as classification or clustering. If any errors occur in the streaming data then the preprocessing mechanisms should be able to handle the errors adaptively. Many preprocessing techniques are implemented for preprocessing streaming data, to adapt dynamic changes, and to handle different situations. The problem is the preprocessing systems does not efficiently handles different situations and adapt to changes for streamed sensor data. In this research, a new adaptive preprocessing mechanism is proposed that will efficiently handle changes in the incoming streaming data and scenarios are implemented to decouple the preprocessor and predictor in different situations for increasing the prediction accuracy. The proposed system uses PCA (Principal Component Analysis) as preprocessor and Hyperbolic Hopfield Neural Network (HHNN) as predictor. This method provides an efficient and adaptive preprocessing of streaming data.
|Title of host publication
|Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015
|Institute of Electrical and Electronics Engineers Inc.
|Published - Sept 28 2015
|9th IEEE International Conference on Intelligent Systems and Control, ISCO 2015 - Coimbatore, India
Duration: Jan 9 2015 → Jan 10 2015
|9th IEEE International Conference on Intelligent Systems and Control, ISCO 2015
|1/9/15 → 1/10/15
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
- Electrical and Electronic Engineering
- Control and Systems Engineering