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

How Functional Complexity affects the Scalability-Energy Efficiency Trade-Off of HCC WSN Clustering