Relation between Functional Complexity, Scalability and Energy Efficiency in WSNs

TL;DR

This paper uses complex systems science to analyze how the functional complexity of clustering in wireless sensor networks influences their scalability and energy efficiency, revealing that higher complexity correlates with better scalability but lower energy efficiency.

Contribution

It introduces a novel application of functional topology and complexity metrics to understand the trade-offs in WSN clustering mechanisms.

Findings

Higher functional complexity correlates with increased scalability.

Higher functional complexity is associated with decreased energy efficiency.

Functional topology analysis provides insights into network property relationships.

Abstract

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.