Skip to Main Content
Spatio-temporal redundancy information in sensor network data is brought about thanks to stochastic deployment, dense sensing and dynamic topology. Moreover, typical sensor data are not only irregularly spaced but also temporal irregular, which is incompatible with standard wavelet algorithm. As for limited resource of wireless sensor network: power, computation, communication and etc, tradeoff of computation and communication is vital for budget for energy consumes and life prolongs. An efficient scheme of collaborative information and signal processing (CISP) is put forward for hierarchical data aggregation and spatio-temporal decorrelation based on in-cluster distributed second-generation wavelet lift-scheme. This ubiquitous local algorithm not only decrease sharply the communication cost when transmitting information to sink node with approximate information reserved, but also deals with the fundamental issue of spatio-temporal irregular samples.