2026 International Conference on Networks Computers and Communications (ICNCCom 2026)

The rapid growth of large-scale distributed systems has intensified the need for smart network monitoring techniques to ensure reliable operation, timely fault detection, and effective resource management. Monitoring complex networks require scalable methodologies capable of uniquely identifying and tracking network components while minimizing communication overhead. In this context, graph-theoretic approaches provide a structured framework for modeling network architectures and evaluating their monitoring capabilities. By identifying strategically important nodes within a network, distance-based graph parameters can support smart network monitoring, fault diagnosis, and efficient network management in distributed systems. Metric dimension of a graph G is the minimum number of vertices (metric basis) required to uniquely identify all the other vertices based on their distance vectors. The total metric bases of G is termed as BIGS index. A vertex x is said to have resolving power r if the vertex x appears in r different metric bases. The total number of metric bases that contain the vertices with maximum resolving power is defined as PJS index, named after the Mathematician Peter John Slater. This study focuses on analyzing resolving power, PJS index and Beacon Partition number in Honeycomb networks, Enhanced Honeycomb networks, Honeycomb Torus networks and Extended Honeycomb Torus networks, aiming to understand vertex significance in metric resolvability.
 

Metric bases, metric dimension, resolving power, PJS Index, Beacon Partition number, Honeycomb networks and its variants, BIGS algorithm, Smart network monitoring