Time series forecasting for energy-efficient organization of wireless sensor networks

Due to their wide potential applications, wireless sensor networks have recently received tremendous attention. The strict energy constraints of sensor nodes result in the great challenges for energy efficiency. This paper investigates the energy efficiency problem and proposes an energy-efficient organization method with time series forecasting. The organization of wireless sensor networks is formulated for target tracking. Target model, multi-sensor model and energy model are defined accordingly. For the target tracking application, target localization is achieved by collaborative sensing with multi-sensor fusion. The historical localization results are utilized for adaptive target trajectory forecasting. Empirical mode decomposition is implemented to extract the inherent variation modes in the time series of a target trajectory. Future target position is derived from autoregressive moving average (ARMA) models, which forecast the decomposition components, respectively. Moreover, the energy-efficient organization method is presented to enhance the energy efficiency of wireless sensor networks. The sensor nodes implement sensing tasks according to the probability awakening in a distributed manner. When the sensor nodes transfer their observations to achieve data fusion, the routing scheme is obtained by ant colony optimization. Thus, both the operation and communication energy consumption can be minimized. Experimental results verify that the combination of the ARMA model and empirical mode decomposition can estimate the target position efficiently and energy saving is achieved by the proposed organization method in wireless sensor networks.