Adaptive Context-Aware Secure Edge Reasoning Framework for Intelligent Traffic Monitoring Using Drone and Satellite Image Analytics

Abstract

Intelligent traffic monitoring is a key element in smart transportation systems, and the ability to perform a fast and secure analysis of heterogeneous visual information is crucial for timely traffic control and incident handling. While complementary drone- and satellite-based perspectives provide local and global understanding, current approaches to fusion based on Edge AI typically rely on static fusion strategies, have limited adaptability to variable traffic conditions, and are vulnerable to attacks that compromise inference integrity. In response to this, the paper introduces an Adaptive Context-Aware Secure Edge Reasoning Framework (ACSERF) to overcome these challenges in intelligent traffic monitoring through drone and satellite image analytics. Both the proposed cross-scale reasoning mechanism and cyber-confidence are dynamic models. The former models cross-scale spatial relationships between drone and satellite observations dynamically, while the latter estimates the cyber-confidence for the reliability of visual information before inference at the edge. Moreover, an edge optimization strategy that adaptively allocates resources allows computational loads to be adjusted, enabling offloaded computation processes to run at low latency without compromising their detection capability. It was tested with available/available drone and satellite traffic image datasets and tested under a variety of traffic densities and environmental conditions. From experimental results, an overall accuracy of 99.03%, precision of 98.85%, recall of 98.71%, F1-score of 98.78%, and mAP of 98.90% were achieved while reducing inference latency by 30.9% and computational overhead by 26.4% over the previously reported methods. The cybersecurity module also achieved a 98.12% detection rate and a 1.57% false alarm rate, enhancing its ability to withstand data-injection and adversarial attacks. These results show that the proposed ACSERF can be an efficient, scalable, and cyber-resilient solution for next-generation intelligent traffic monitoring in smart transportation environments.