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Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures.
George Tzagkarakis1, Pavlos Charalampidis1, Stylianos Roubakis1
1Institute of Computer Science, Foundation for Research and Technology-Hellas, GR70013 Heraklion, Greece.
Sensors (Basel, Switzerland)
|June 14, 2020
Summary
Compressive sensing (CS) in smart water networks significantly reduces data compression time and energy use. This method enables efficient data handling and weak encryption for battery-powered sensor nodes.
Area of Science:
- Environmental Engineering
- Computer Science
- Signal Processing
Background:
- Smart water distribution networks (WDN) utilize sensor nodes for monitoring.
- Battery-powered nodes limit data sampling rates due to energy constraints.
- Compressive sensing (CS) offers a solution for reduced bandwidth and storage.
Purpose of the Study:
- Investigate the practical benefits of CS on real sensing devices in smart water networks.
- Evaluate CS for execution speedup and energy reduction.
- Assess CS for data security in WDN monitoring.
Main Methods:
- Implemented a compressive sensing (CS) scheme on real sensing devices.
- Conducted experimental evaluations comparing CS with lossless compression.
- Analyzed compression execution times, energy consumption, and reconstruction fidelity.
Main Results:
- CS reduced compression execution times by approximately 50%.
- Significant energy savings were achieved with CS compared to lossless compression.
- CS enabled weak data encryption without additional hardware or software.
Conclusions:
- CS implementation offers substantial performance and energy benefits for smart WDN monitoring.
- CS provides a viable solution for enhancing data management and security in WDN.
- CS technology can improve the efficiency and capabilities of smart water networks.
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