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.
@article{arxiv.1610.05970,
title = {Relation between Functional Complexity, Scalability and Energy Efficiency in WSNs},
author = {Merim Dzaferagic and Nicholas Kaminski and Irene Macaluso and Nicola Marchett},
journal= {arXiv preprint arXiv:1610.05970},
year = {2016}
}