Centralized Task Allocation for Multiple UAVs in Time-Constraint Industrial IoT Operations

The industrial Internet of Things (IoT) allows real-time monitoring and operational efficiency by enabling automated processes in industrial environments. In this article, we propose a centralized task assignment framework for industrial IoT scenarios, focusing on light cargo delivery to specific locations via unmanned aerial vehicles (UAVs). The task assignment problem is formulated as a capacitated vehicle routing problem (CVRP) with the objective of minimizing the maximum route distance among all UAVs. To solve CVRP, we employ a learning-based approach using an attention model (AM), which utilizes a deep learning framework with an encoder–decoder architecture to generate optimized UAV routes while satisfying the capacity constraints of UAVs. The AM is trained using policy gradient reinforcement learning (RL) to ensure that the solutions are both efficient and scalable. Numerical results demonstrate the effectiveness of the AM-based framework in delivering solutions that minimize the maximum tour length for deliveries.