An Adaptive-Intelligent Distance-Aware Approach for Dynamic Network Connectivity

Abstract

Optimizing network connectivity characterizes a major challenge in different applications, e.g. IoT, where the efficiency of connectivity under constraints like distance and adaptability is essential. The key objective of the Minimum Spanning Tree (MST) methods is to construct a subset of a graph in which every vertex is connected with the least amount of edge weight and without any cycles. As a robust framework for constructing distance-constrained network connectivity, this research proposes a novel algorithm named Recursive Node Connectivity Algorithm (RNCA). RNCA forms a tree-like networking structure motivated by minimum spanning tree (MST) principles, while explicitly considering distance constraints to ensure feasible communications. The RNCA utilizes repeated pruning and reweighting procedures to create cost-effective and distance-compliant network structures that can ensure flexible and scalable connectivity in dynamic contexts. The performance of RNCA is evaluated through conducting simulations with different network scales. The results show that RNCA achieves high adaptability, scalability, and reduced redundancy. RNCA outperforms Kruskal’s and Prim’s MST algorithms by reducing recomputed links during node updates by up to 80%. It also ensures minimum change in network connections when nodes are added or removed from the original network topology. Therefore, it can be considered as a transformative solution for many modern network applications, such as transportation systems, WSNs, and IoT.