Contemporary water distribution networks exploit
Internet of Things (IoT) technologies to monitor and control
the behavior of water network assets. Smart meters/sensor
and actuator nodes have been used to transfer information
from the water network to data centers for further analysis.
Due to the underground position of water assets, many water
companies tend to deploy battery driven nodes which last
beyond the 10-year mark. This prohibits the use of high-sample
rate sensing therefore limiting the knowledge we can obtain
from the recorder data. To alleviate this problem, efficient
data compression enables high-rate sampling, whilst reducing
significantly the required storage and bandwidth resources
without sacrificing the meaningful information content. This
paper introduces a novel algorithm which combines the accuracy
of standard lossless compression with the efficiency
of a compressive sensing framework. Our method balances
the tradeoffs of each technique and optimally selects the best
compression mode by minimizing reconstruction errors, given
the sensor node battery state. To evaluate our algorithm, real
high-sample rate water pressure data of over 170 days and 25
sensor nodes of our real world large scale testbed was used.
The experimental results reveal that our algorithm can reduce
communication around 66% and extend battery life by 46%
compared to traditional periodic communication techniques.