Design and Implementation of an Optimal N-Edge Connected Networks for IOT Based Random Mesh

Abstract

Introduction: In IOT, very few problems were solved by using optimal n-edge connected networks; however, this is the first attempt to increase the performance of IOT by optimal n-edge. The major focus of this study was to design an optimal edge connectivity because it plays an important role in the fault tolerance of a network. These models are designed by studying standard network models like Static, Generative, and Evolving network models. Methods: In this model, initially, take a mesh network, i.e., Gmesh, which needs to be optimized. Divide Gmesh into clusters were i=1, 2..., n and n=number of possible clusters for Gmesh. Edges Ei, j which are not in Clusters, add them into list Rem. Choose the optimal networks like 1-edge, 2-edge, 3- edge, and Trimet of clusters, which are required by the user. Results: Edges count is less for 1-edge compared to 2-edge, 3-edge, and random mesh. Whereas the number of edges for Trimet of clusters lies between 1-edge and 2-edge. The diameter of 1-edge is high compared to all other networks, as the number of nodes is increased. As the diameter is used to find the shortest distance between the two most distant nodes in the network, it is low for Trimet of clusters compared to 1-edge, 2-edge, 3-edge, and mesh network. Conclusion: Proposed models 1-edge, 2-edge, 3-edge, and TGO-edge are compared with invariant network science parameters of random mesh. The 1-edge connected model has a high diameter and average shortest path length. The 2-edge connected model has a high diameter and average shortest path length. In a 3-edge connected model, diameter and average shortest path length are high, whereas it has a low density and average degree as compared to random mesh. TGO-edge model has optimal results over random mesh in all aspects except average degree.