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

TL;DR

This paper investigates how the complexity of communication patterns in Wireless Sensor Networks influences their scalability and energy efficiency, using a complex systems approach and functional topology graphs.

Contribution

It introduces a novel application of functional complexity metrics to understand WSN properties, revealing the relationship between local interactions and global network behavior.

Findings

Higher functional complexity correlates with increased scalability.

Higher functional complexity correlates with decreased energy efficiency.

The approach provides insights into organizational patterns affecting network performance.

Abstract

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