Background:
Wireless Sensor Networks (WSNs) have been researched to be one of the ground-breaking
technologies for the remote monitoring of pipeline infrastructure of the Oil and Gas industry. Research have also shown that
the preferred deployment approach of the sensor network on pipeline structures follows a linear array of nodes, placed a
distance apart from each other across the infrastructure length. The linear array topology of the sensor nodes gives rise to the
name Linear Wireless Sensor Networks (LWSNs) which over the years have seen themselves being applied to pipelines for
effective remote monitoring and surveillance. This paper aims to investigate the energy consumption issue associated with
LWSNs deployed in cluster-based fashion along a pipeline infrastructure.
Methods:
Through quantitative analysis, the study attempts to approach the investigation conceptually focusing on
mathematical analysis of proposed models to bring about conjectures on energy consumption performance.
Results:
From the derived analysis, results have shown that energy consumption is diminished to a minimum if there is a
sink for every placed sensor node in the LWSN. To be precise, the analysis conceptually demonstrate that groups containing
small number of nodes with a corresponding sink node is the approach to follow when pursuing a cluster-based LWSN for
pipeline monitoring applications.
Conclusion:
From the results, it is discovered that energy consumption of a deployed LWSN can be decreased by creating
groups out of the total deployed nodes with a sink servicing each group. In essence, the smaller number of nodes each group
contains with a corresponding sink, the less energy consumed in total for the entire LWSN. This therefore means that a sink
for every individual node will attribute to minimum energy consumption for every non-sink node. From the study, it can be
concurred that energy consumption of a LWSN is inversely proportional to the number of sinks deployed and hence the
number of groups created.
Minimum energy transmission forest‐based geocast in
software‐defined
wireless sensor networks
