Relation between Functional Complexity, Scalability and Energy Efficiency in WSNs

Studies of clustering in Wireless Sensor Networks (WSNs) usually tackle the problems of designing new algorithms and compare them based on a set of properties (e.g. energy efficiency, scalability), lacking the understanding of the underlying mechanisms and communication patterns that lead to these properties. Our approach tackles this lack of understanding by applying techniques developed by complex systems scientists. Functional topology graphs, which describe the interactions between system parts, are used to represent different implementations of clustering in WSNs. We employ a complexity metric - functional complexity (C-F) - to quantify the potential of the functional topology to transport information. Our analysis highlights the trade-off between scalability and energy efficiency, showing that higher values of C-F indicate higher scalability and lower energy efficiency.