Publication

Abstract : Background: Graph analytics is one of the foremost established and unique strategies utilized in taking care of present-day designing issues. In this study, this procedure was applied to networks. The connectivity of gadgets is one of the intense issues distinguished in wireless systems. To deal with this issue, a unique Fibonacci-based TGO was proposed for a superior network. Methods: The proposed model attempts to construct a trimet graph based on the Fibonacci arrangement, implying that a cluster is formed with 3, 5, 8, 13, 21... hubs. To frame Fibonacci-based TGO, each of these hubs is recursively connected with a trimet diagram. For the random regular graph, the practical mesh is invariant. Edges, diameter, average degree, average clustering, density, and average shortest path are currently being compared for both meshes. Results: Fibonacci TGO has approximately 50 edges at 100 nodes and a constant diameter of 4. The average degree of Fibonacci TGO is less, which is approximately 3, having 0.7 high average clustering over random regular. As the number of nodes increases, the density decreases.TGO is having a better path than the random regular model. Finally, Fibonacci TGO mesh has better performance and connectivity over real-time meshes in wireless networks. Conclusion: We have proposed Fibonacci-based TGO mesh in the following steps. This formation is split into two stages. Fibonacci-dependent trimets based on the input nodes are created in the first stage, with 3, 5, 8, 13, and 21... nodes. These trimets will be connected in the second step to create a Fibonacci-based TGO. Both meshes are now being studied using network science parameters. In any scenario, Fibonacci-based TGO has better connectivity over the real-time random mesh. The NetworkX package in the Python language is used to produce the results automatically.