  In order to understand the underlying mechanisms that lead to certain network
properties (i.e. scalability, energy efficiency) we apply a complex systems
science approach to analyze clustering in Wireless Sensor Networks (WSN). We
represent different implementations of clustering in WSNs with a functional
topology graph. Different characteristics of the functional topology provide
insight into the relationships between system parts that result in certain
properties of the whole system. Moreover, we employ a complexity metric -
functional complexity (C_F) - to explain how local interactions give rise to
the global behavior of the network. Our analysis shows that higher values of
C_F indicate higher scalability and lower energy efficiency.
