Light-weight Online Predictive Data Aggregation for Wireless Sensor Networks
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
| Hi-index | 0.00 |
Data reduction strategy is one of the schemesemployed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application of WSN are compared with well-known prediction algorithms such as ARIMA, LMS and WMA models. We implemented a real-world deployment using 32bit mote-class device. Overall, up to 96%transmission reduction is achieved using our Naive method, while still able to maintain a considerable level of accuracy at 0.5 degrees error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.