Energy performance of Internet of Things (IoT) networks for pipeline monitoring

Pipelines are the most convenient ways to transport
fluids (e.g., water, oil, and gas). However, leakage of fluids
into the environment results in resource wastage (primarily
water, which is becoming a scarce resource) and environmental
pollution (in the case of leakage of toxic fluids like oil and gas).
Emerging technologies like the Internet of Things (IoT), Wireless
Sensor Networks (WSNs), Artificial Intelligence (AI), distributed
computing, and cloud computing enable continuous monitoring
of pipelines to detect leakages and corrosion on the pipeline.
The main challenge with using battery-powered sensor nodes to
monitor pipelines is the energy constraint, necessitating frequent
battery replacement. Thus, there is a need to develop energysaving
mechanisms to prolong the lifetime of these sensor nodes.
In this paper, we use the diffusion approximation modelling
framework in which the data from the experimental testbed are
used to model the dynamics of the battery’s energy content and
to estimate the mean and variance of the device’s lifetime. The
novelty in the proposed diffusion model of the battery of an
IoT node is the introduction of multiple energy thresholds that
split the energy state-space of the battery into multiple energysaving
regimes. As the battery discharges, the node gradually
transitions into energy-saving regimes by reconfiguring some of
its parameters to reduce energy consumption (sometimes at the
cost of trading off some performance metrics). We investigate the
impact of energy-saving regimes or the number of thresholds on
the node’s lifetime.