SCGG: Smart City Network Topology Graph Generator

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ABSTRACTSmart cities use information and communication technology to promote citizen welfare and economic growth within a sustainable environment. To guarantee that different urban actors, including people, devices, companies, and governments, can communicate efficiently, securely, and reliably, a robust, adaptable network infrastructure is required. However, the increasing complexity of the systems involved poses a challenge to smart city network modeling. Network topology generators produce synthetic networks that can reflect the underlying properties of real‐world networks, providing a practical approach to designing, testing, and implementing complex systems such as smart cities, yet the limited number of network topology generators for smart city applications has long prevented the proper development, investigation, and evaluation of various network configurations. In this article, a novel Smart City Network Topology Graph Generator (SCGG) is proposed to create a pseudorandom topology that mimics real smart city networks. The main goal of SCGG is to generate a network topology for smart cities that captures the interconnectivity of several communication technologies, such as wireless sensor networks (WSN), Internet of Things (IoT), and cellular networks. The SCGG system is characterized by the number of clusters, the average number of nodes, the number of layers, and the node density. The general network architecture and path‐related variables of the generated topologies are evaluated based on different graph theory measures, focusing on both global graph‐level characteristics and local node‐level features. The experimental results, demonstrating high natural connectivity and a low spectral radius value, offer a reliable tool for optimizing and strengthening the behavior and performance of smart city networks under different conditions to improve their robustness, minimize the probability of disruptions or failures, and enhance overall efficiency to ensure a resilient network.