In modern life, there is invisible data being continuously generated, data that if collected and processed can detect risks and changes and enable us to mitigate their effect. Thus, there is an essential need to sense everything around us in order to make better use of it. This lead us to an era known as “sensing-era” in which wireless sensor networks (WSN) play a vital role in the monitoring of natural and artificial environments. Indeed, the collection and transmission of huge amounts of redundant data by sensor nodes will lead to a faster consumption of their limited battery power, which is sometimes difficult to replace or recharge, reducing the overall lifetime of the network. Therefore, an effective way to increase lifetime by saving energy is to reduce the amount of transmitted data by eliminating redundancy along the path to the sink. In this paper, we propose a Zoom-In Zoom-Out (ZIZO) mechanism aimed to minimize data transmission in WSN. ZIZO works on two WSN levels: on the sensor level where we propose a compression method called index-bit-encoding (IBE) in order to aggregate similar readings before sending them to the second network level, e.g. cluster-head (CH). The CH searches then for correlation among node data in order to optimize the sampling rate of the sensors in the cluster through a process called sampling rate adjustment (SRA). We evaluate the performance of our mechanism based on both simulations and experiments while the obtained results are compared to other existing techniques, and we show reduced energy consumption by up to 90% in some cases.