Performance Evaluation of Pathfinding Algorithms for Intelligent Routing in High-Mobility IoT Edge Networks

Efficient route discovery is a key analytical challenge in high-mobility IoT environments such as Vehicular Ad-hoc networks (VANETs), where frequent topology changes degrade communication reliability. This study leverages intelligent data analytics to compare heuristic (A, Greedy Best-First Search (GBFS )) and non-heuristic (Dijkstra, Bellman-Ford) algorithms within a grid-based vehicular network under varying densities. Using metrics such as Route Discovery Time (RDT), Route Discovery Messages (RDM), and Path Length (PL), we evaluate their performance in both sparse and dense topologies. Results show that in sparse networks, heuristic algorithms — particularly A — offer the best trade-of between discovery efficiency and path optimality. In contrast, GBFS sacrifices accuracy for speed, while non-heuristic methods incur higher overhead. In dense scenarios, A maintains superior performance, demonstrating scalability and robustness. These insights ofer valuable predictive analytics for designing routing protocols in dynamic IoT environments, enabling intelligent decision-making for connected vehicle systems.