Contemporary water distribution networks exploit information communication technologies (ICT) to monitor and control the behavior of water network assets. Limited capability and typically battery powered low-resourced devices, such as smart meters/sensors, have been used to transfer information from the water network to data centers for further analysis. Many water companies deploy devices aiming to last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from this data. However, data reduction techniques with minimal information loss can overcome this problem. In this paper, we present a self-adaptive scheme that reduces the amount of transmitted data, thus extending the battery life of sensor nodes, while still maximizing the received information to data centers. To achieve these goals, we exploit the power of compressive sensing (CS), which enables significant compaction of the original information content in a few random incoherent projections. Sparsity of the recorded data streams, which is a necessary condition for successful CS reconstruction, is achieved via the transformation of the original data into an appropriate transform domain. Using over 170 days of real high-sample rate water pressure data from 25 sensor nodes of our large scale testbed in Bristol area, we verify the efficiency of our CS-based algorithm in reducing significantly the data volume, and thus extending the battery life of sensor nodes. In addition, we demonstrate that our system supports self-tuning and automatic reconfiguration as the nature of incoming data changes over time.