A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks
Wireless sensor networks rely on Puzzles sensor devices deployed in an environment to support sensing and monitoring, including temperature, humidity, motion, and acoustic.Here, we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear function