Uniquely identifying vertices and edges of a heptagonal snake graph using distance formulas

Consider a city with a network of roads where surveillance cameras are positioned at key intersections. To uniquely determine the position of any car, the minimal number of cameras needed is determined by using the metric dimension of the network. Similarly, if we desire to differentiate each individual road segment using cameras, we depend on the edge metric dimension. These concepts are essential in applications such as transportation planning, autonomous navigation and network monitoring. Let $ G = (V(G), E(G)) $ be a connected graph with vertex set $ V(G) $ and edge set $ E(G) $, a subset $ R\subseteq V(G) $ is called a resolving set, if each vertex of $ G $ has unique distance with respect to $ R $. The cardinality of the smallest resolving set is called the metric dimension, denoted as $ dim(G) $. Similarly, a set of vertices $ R \subseteq V(G) $ is called an edge resolving set if for every pair of distinct edges $ e_1, e_2 \in E(G) $, there exists a vertex $ v \in R $ such that $ d(e_1, v) \neq d(e_2, v) $, where $ d(e, v) $ is defined as $ d(e = uw, v) = \min\{ d(u, v), d(w, v) \} $. The edge metric dimension is defined as the number of elements in the smallest edge resolving set. Finding these dimensions is an NP-hard problem, making efficient computations precious. In this article, we explored the metric dimension, edge metric dimension, partition dimension and edge partition dimension of the heptagonal snake graph $ (HPS_k) $.