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As a typical Cyber-Physical System (CPS), smart water distribution networksrequire monitoring of underground water pipes with high sample rates forprecise data analysis and water network control. Due to poor undergroundwireless channel quality and long-range communication requirements, hightransmission power is typically adopted to communicate high-speed sensor datastreams; posing challenges for long term sustainable monitoring. In this paper,we develop the first sustainable water sensing system, exploiting energyharvesting opportunities from water flows. Our system does this by schedulingthe transmission of a subset of the data streams, while other correlatedstreams are estimated using auto-regressive models based on the sound-velocitypropagation of pressure signals inside water networks. To compute the optimalscheduling policy, we formalize a stochastic optimization problem to maximizethe estimation reliability, while ensuring the system's sustainable operationunder dynamic conditions. We develop Data Transmission Scheduling (DTS), anasymptotically optimal scheme; and FAST-DTS, a lightweight online algorithmthat can adapt to arbitrary energy and correlation dynamics. Using over 170days of real data from our smart water system deployment and conductingin-vitro experiments to our small-scale testbed; our evaluation demonstratesthat Fast-DTS significantly outperforms three alternatives, considering datareliability, energy utilization, and sustainable operation.

Reliability or Sustainability: Optimal Data Stream Estimation and Scheduling in Smart Water Networks