Trade-offs of Forecasting Algorithm for Extending WSN Lifetime in a Real-World Deployment2013 IEEE International Conference on Distributed Computing in Sensor Systems

Data reduction strategy is one of the schemes employed 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 C error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.