Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks
AbstractAn important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method. View Full-Text
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Wang, X.; Ma, J.-J.; Ding, L.; Bi, D.-W. Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks. Sensors 2007, 7, 2779-2807.
Wang X, Ma J-J, Ding L, Bi D-W. Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks. Sensors. 2007; 7(11):2779-2807.Chicago/Turabian Style
Wang, Xue; Ma, Jun-Jie; Ding, Liang; Bi, Dao-Wei. 2007. "Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks." Sensors 7, no. 11: 2779-2807.