Iot-enabled path optimization for smart logistics using Ant Colony Algorithm

The logistics sector faces persistent challenges in efficient and reliable route planning, particularly in fast-changing, data-intensive environments. To address these, we propose an IoT-enabled logistics framework built on a layered fog-cloud architecture for real-time data acquisition, processing, and decision-making. Leveraging an optimized Ant Colony Algorithm (ACA), the system identifies the most efficient transportation routes using live operational data. Comparative analysis with traditional optimization methods shows the ACA-based approach consistently reduces delivery delays and improves route selection under diverse operational constraints. By integrating IoT infrastructure with ACA optimization, the framework offers a scalable, adaptive, and data-driven solution that enhances service quality and operational efficiency in modern logistics.