Prolonging the lifetime of wireless sensor networks using light-weight forecasting algorithms

The longevity of wireless sensor network (WSN) deployments is often crucial for real-time monitoring applications. Minimizing energy consumption by utilizing intelligent information processing is one of the main ways to prolong the lifetime of a network deployment. Data streams from the sensors need to be processed within the resource constraints of the sensing platforms to reduce the energy consumption associated with packet transmission. In this paper we carried out both simulation and real-world implementation of light-weight adaptive models to achieve a prolonged WSN lifetime. Specifically, we propose a Naive model that incurs virtually no cost with low memory footprint to realize this goal. Our results show that, despite its minimal complexity, the Naive model is robust when compared with other well-known algorithms used for prediction in WSNs. We show that our approach achieves up to 96% communication reduction, within 0.2 degrees error bound with no significant loss in accuracy and it is comparable in performance to the more complex algorithms like Exponential Smoothing (ETS).