Cloud-edge collaboration-based task offloading strategy in railway IoT for intelligent detection

Driven by technologies such as deep learning, online detection equipment can perform comprehensive and continuous monitoring of high-speed railways (HSR). However, these detection tasks in the railway Internet of Things (IoT) are typically computation-intensive and delay-sensitive, that makes task processing challenging. Meanwhile, the dynamic and resource-constrained nature of HSR scenarios poses significant challenges for effective resource allocation. In this paper, we propose a cloud-edge collaboration architecture for deep learning-based detection tasks in railway IoT. Within this system model, we introduce a distributed inference mode that partitions tasks into two parts, offloading task processing to the edge side. Then we jointly optimize the computing offloading strategy and model partitioning strategy to minimize the average delay while ensuring accuracy requirements. However, this optimization problem is a complex mixed-integer nonlinear programming (MINLP) issue. We divide it into two sub-problems: computing offloading decisions and model partitioning decisions. For model partitioning, we propose a Partition Point Selection (PPS) algorithm; for computing offloading decisions, we formulate it as a Markov Decision Process (MDP) and solve it using DDPG. Simulation results demonstrate that PPS can rapidly select the globally optimal partition points, and combined with DDPG, it can better adapt to the offloading challenges of detection tasks in HSR scenarios.