A GAT based approach for shortest path prediction in distributed IoT dynamic environment

In large-scale directed multi-criteria weighted graph systems, predicting the cost of possible paths or operations is a significant challenge, particularly in areas such as logistics, resource allocation, and workflow management. The non-linear connections and structural information present in such graph data are difficult for current cost prediction methods to handle, which leads to lower accuracy. To improve cost prediction, we suggest a novel method that makes use of graph attention networks (GAT). Our approach uses a multi-head attention mechanism to selectively aggregate important adjacent information, allowing the model to learn feature-rich, localized representations for every node and edge in the weighted graph structure. In particular, the GAT layers are implemented to reflect how the total cost of a directed path or sub-graph operation is influenced by the weights of nearby edges. Our GAT-based model offers a more reliable and accurate framework for cost prediction in dynamic environments. Our experimental results show that the predicted paths match the ground truth paths selected by the Dijkstra algorithm.