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.
An Application-Specific Forecasting Algorithm for Extending WSN Lifetime2013 IEEE International Conference on Distributed Computing in Sensor Systems / Femi A. Aderohunmu;Giacomo Paci;Davide Brunelli;Jeremiah D. Deng;Luca Benini;Martin Purvis. - STAMPA. - (2013), pp. 374-381. (Intervento presentato al convegno 9th IEEE International Conference on Distributed Computing in Sensor Systems, DCoSS 2013 tenutosi a Cambridge, MA; United States nel 20-23 May 2013) [10.1109/DCOSS.2013.51].
An Application-Specific Forecasting Algorithm for Extending WSN Lifetime2013 IEEE International Conference on Distributed Computing in Sensor Systems
PACI, GIACOMO;BRUNELLI, DAVIDE;BENINI, LUCA;
2013
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
