Priority-Based Deep Learning Inference On Edge Devices for Military Surveillance

This work proposes a priority scheduling formula for Quick Task Execution in a military battlefield including; image segmentation, classification, and detection which are deep learning tasks that face challenges in edge AI due to their computational requirements. Comparisons using real-world simulation scenarios show the efficiency of the proposed formula over traditional scheduling techniques by proving that it can immediately allocate higher priority tasks to resource- limited endpoints on the edge of a network. In a way, the marked approach ensures optimized task execution within queues as well as task importance matched with the computational capacity, which reduces processing time and corresponding delays, and thus improves the general efficiency. This method is scalable and reliable for risk management in military surveillance and is also suitable for any field such as disaster response and smart city planning.