  Even though clustering algorithms in Wireless Sensor Networks (WSN) are a
well investigate subject, the increasing interest in the Internet of Things
(IoT) and 5G technologies has precipitated the need of new ways to comprehend
and overcome a new set of challenges. While studies mainly propose new
algorithms and compare these algorithms based on a set of properties (e.g.
energy efficiency, scalability), none of them focuses on the underlying
mechanisms and organizational patterns that lead to these properties. We
address this lack of understanding by applying a complex systems science
approach to investigate the properties of WSNs arising from the communication
patterns of the network nodes. We represent different implementations of
clustering in WSNs with a functional topology graph. Moreover, we employ a
complexity metric - functional complexity (CF) - to explain how local
interactions give rise to the global behavior of the network. Our analysis
shows that higher values of CF indicate higher scalability and lower energy
efficiency.
