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Previous studies have indicated that data compression in wireless sensor networks is not always beneficial to energy conservation due to the additional computational energy costs. This work gives an energy-efficient arbitration mechanism that enhances the performance of compression algorithms by avoiding unnecessary energy losses. The adaptive compression arbitration system uses a new prediction modeling and adaptation. Tests show that the modeling method gives better predictions with the adaptive mechanism needing less data for modeling, which allows on-line prediction modeling instead of an off-line training process. Moreover, adaptive adjustments of the model parameters enable the system to keep good arbitration precision even as the data characteristics or applications change.
Previous studies have indicated that data compression in wireless sensor networks is not always beneficial to energy conservation due to the additional computational energy costs. This work gives an energy-efficient arbitration mechanism that enhances the performance of compression algorithms by avoiding unnecessary energy losses. The adaptive Compression arbitration system uses a new prediction modeling and adaptation. Moreover, Adaptive method requires better predictions with the adaptive method than an off-line training process. of the model parameters enable the system to keep good arbitration precision even as the data characteristics or applications change.