Energy preservation in large-scale wireless sensor network utilizing distributed compressive sensing

In large scale wireless sensor network (WSN) energy reservation is crucial, as in such an environment sensors cannot be periodically maintain. Therefore we investigate the opportunity to reduce the power consumption by reducing the data rate traffic of the network. This is done utilizing either data correlation and sparsity in one dimension or the spatial sparsity among clustered sensor nodes. We found that the data rate can be significantly reduced with minimum recovery error; this extend the life time of the network. Moreover, utilizing the predefined wireless sensing clustering assuming that the nodes in a cluster are sharing most of the sparse supports. Thus, distributed compressive sensing in such a case enhances the whole life time. Finally, we investigated how to adaptively compromise between the measurement error and energy reduction to have a moderate network life time with an accepted error rate.