Transformer-Based Anomaly Detection With Distributed Fiber-Optic Intelligence in End-Edge-Cloud Systems

Cyber–physical–social systems (CPSSs) emerged as a novel paradigm addressing the growing interactions among physical, social, and cyber domains. Many CPSS challenges were mathematically modeled as optimization problems, with a significant portion requiring multiobjective optimization approaches. To address inefficient learning caused by large-scale distributed sensor data in CPSS, a transformer-based anomaly detection framework for end-edge-cloud systems was proposed. For specific anomaly signals, an improved anomaly detection matrix was developed through systematic calculation of attention weights and corresponding scores. A distributed intelligence architecture integrating transformer-based anomaly detection was implemented in edge nodes for fiber-optic sensors. The proposed artificial Intelligence of Things (AIoT) based distributed edge computing model dynamically allocated tasks among idle, partially available, and fully utilized nodes through optimized resource management in task transmission. A high-efficiency allocation strategy was designed to optimize task processing rates and enable edge nodes to dynamically balance task execution and transmission without compromising detection accuracy. Experimental results demonstrated the effectiveness of both the enhanced transformer method and the computational pipeline across end-edge-cloud architectures. Compared with the vision transformer baseline, our method achieved 3.62% higher accuracy and 3.07% improved recall. Computational time was reduced by approximately 50% while memory consumption decreased by 40%. The proposed approach showed significant potential for anomaly detection in AIoT applications

Anomaly detection · Computational intelligence · Edge computing · Intrusion detection system · Task Analysis · Transformer · Anomaly Detection Techniques and Applications · IoT and Edge/Fog Computing · Software System Performance and Reliability